Linking Resting-State Networks in the Prefrontal Cortex to Executive Function: A Functional Near Infrared Spectroscopy Study
Identifieur interne : 000987 ( Pmc/Corpus ); précédent : 000986; suivant : 000988Linking Resting-State Networks in the Prefrontal Cortex to Executive Function: A Functional Near Infrared Spectroscopy Study
Auteurs : Jia Zhao ; Jiangang Liu ; Xin Jiang ; Guifei Zhou ; Guowei Chen ; Xiao P. Ding ; Genyue Fu ; Kang LeeSource :
- Frontiers in Neuroscience [ 1662-4548 ] ; 2016.
Abstract
Executive function (EF) plays vital roles in our everyday adaptation to the ever-changing environment. However, limited existing studies have linked EF to the resting-state brain activity. The functional connectivity in the resting state between the sub-regions of the brain can reveal the intrinsic neural mechanisms involved in cognitive processing of EF without disturbance from external stimuli. The present study investigated the relations between the behavioral executive function (EF) scores and the resting-state functional network topological properties in the Prefrontal Cortex (PFC). We constructed complex brain functional networks in the PFC from 90 healthy young adults using functional near infrared spectroscopy (fNIRS). We calculated the correlations between the typical network topological properties (regional topological properties and global topological properties) and the scores of both the Total EF and components of EF measured by computer-based Cambridge Neuropsychological Test Automated Battery (CANTAB). We found that the Total EF scores were positively correlated with regional properties in the right dorsal superior frontal gyrus (SFG), whereas the opposite pattern was found in the right triangular inferior frontal gyrus (IFG). Different EF components were related to different regional properties in various PFC areas, such as planning in the right middle frontal gyrus (MFG), working memory mainly in the right MFG and triangular IFG, short-term memory in the left dorsal SFG, and task switch in the right MFG. In contrast, there were no significant findings for global topological properties. Our findings suggested that the PFC plays an important role in individuals' behavioral performance in the executive function tasks. Further, the resting-state functional network can reveal the intrinsic neural mechanisms involved in behavioral EF abilities.
Url:
DOI: 10.3389/fnins.2016.00452
PubMed: 27774047
PubMed Central: 5054000
Links to Exploration step
PMC:5054000Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Linking Resting-State Networks in the Prefrontal Cortex to Executive Function: A Functional Near Infrared Spectroscopy Study</title><author><name sortKey="Zhao, Jia" sort="Zhao, Jia" uniqKey="Zhao J" first="Jia" last="Zhao">Jia Zhao</name><affiliation><nlm:aff id="aff1"><institution>School of Computer and Information Technology, Beijing Jiaotong University</institution><country>Beijing, China</country></nlm:aff></affiliation></author><author><name sortKey="Liu, Jiangang" sort="Liu, Jiangang" uniqKey="Liu J" first="Jiangang" last="Liu">Jiangang Liu</name><affiliation><nlm:aff id="aff1"><institution>School of Computer and Information Technology, Beijing Jiaotong University</institution><country>Beijing, China</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2"><institution>Department of Applied Psychology and Human Development, Dr. Eric Jackman Institute of Child Study, University of Toronto</institution><country>Toronto, ON, Canada</country></nlm:aff></affiliation></author><author><name sortKey="Jiang, Xin" sort="Jiang, Xin" uniqKey="Jiang X" first="Xin" last="Jiang">Xin Jiang</name><affiliation><nlm:aff id="aff3"><institution>Department of Computer Science, University College London</institution><country>London, UK</country></nlm:aff></affiliation></author><author><name sortKey="Zhou, Guifei" sort="Zhou, Guifei" uniqKey="Zhou G" first="Guifei" last="Zhou">Guifei Zhou</name><affiliation><nlm:aff id="aff1"><institution>School of Computer and Information Technology, Beijing Jiaotong University</institution><country>Beijing, China</country></nlm:aff></affiliation></author><author><name sortKey="Chen, Guowei" sort="Chen, Guowei" uniqKey="Chen G" first="Guowei" last="Chen">Guowei Chen</name><affiliation><nlm:aff id="aff4"><institution>Department of Psychology, Hangzhou Normal University</institution><country>Hangzhou, China</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff5"><institution>Department of Psychology, Zhejiang Normal University</institution><country>Jinhua, China</country></nlm:aff></affiliation></author><author><name sortKey="Ding, Xiao P" sort="Ding, Xiao P" uniqKey="Ding X" first="Xiao P." last="Ding">Xiao P. Ding</name><affiliation><nlm:aff id="aff2"><institution>Department of Applied Psychology and Human Development, Dr. Eric Jackman Institute of Child Study, University of Toronto</institution><country>Toronto, ON, Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff6"><institution>Department of Psychology, National University of Singapore</institution><country>Singapore, Singapore</country></nlm:aff></affiliation></author><author><name sortKey="Fu, Genyue" sort="Fu, Genyue" uniqKey="Fu G" first="Genyue" last="Fu">Genyue Fu</name><affiliation><nlm:aff id="aff4"><institution>Department of Psychology, Hangzhou Normal University</institution><country>Hangzhou, China</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff5"><institution>Department of Psychology, Zhejiang Normal University</institution><country>Jinhua, China</country></nlm:aff></affiliation></author><author><name sortKey="Lee, Kang" sort="Lee, Kang" uniqKey="Lee K" first="Kang" last="Lee">Kang Lee</name><affiliation><nlm:aff id="aff2"><institution>Department of Applied Psychology and Human Development, Dr. Eric Jackman Institute of Child Study, University of Toronto</institution><country>Toronto, ON, Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff5"><institution>Department of Psychology, Zhejiang Normal University</institution><country>Jinhua, China</country></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">27774047</idno><idno type="pmc">5054000</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054000</idno><idno type="RBID">PMC:5054000</idno><idno type="doi">10.3389/fnins.2016.00452</idno><date when="2016">2016</date><idno type="wicri:Area/Pmc/Corpus">000987</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000987</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Linking Resting-State Networks in the Prefrontal Cortex to Executive Function: A Functional Near Infrared Spectroscopy Study</title><author><name sortKey="Zhao, Jia" sort="Zhao, Jia" uniqKey="Zhao J" first="Jia" last="Zhao">Jia Zhao</name><affiliation><nlm:aff id="aff1"><institution>School of Computer and Information Technology, Beijing Jiaotong University</institution><country>Beijing, China</country></nlm:aff></affiliation></author><author><name sortKey="Liu, Jiangang" sort="Liu, Jiangang" uniqKey="Liu J" first="Jiangang" last="Liu">Jiangang Liu</name><affiliation><nlm:aff id="aff1"><institution>School of Computer and Information Technology, Beijing Jiaotong University</institution><country>Beijing, China</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2"><institution>Department of Applied Psychology and Human Development, Dr. Eric Jackman Institute of Child Study, University of Toronto</institution><country>Toronto, ON, Canada</country></nlm:aff></affiliation></author><author><name sortKey="Jiang, Xin" sort="Jiang, Xin" uniqKey="Jiang X" first="Xin" last="Jiang">Xin Jiang</name><affiliation><nlm:aff id="aff3"><institution>Department of Computer Science, University College London</institution><country>London, UK</country></nlm:aff></affiliation></author><author><name sortKey="Zhou, Guifei" sort="Zhou, Guifei" uniqKey="Zhou G" first="Guifei" last="Zhou">Guifei Zhou</name><affiliation><nlm:aff id="aff1"><institution>School of Computer and Information Technology, Beijing Jiaotong University</institution><country>Beijing, China</country></nlm:aff></affiliation></author><author><name sortKey="Chen, Guowei" sort="Chen, Guowei" uniqKey="Chen G" first="Guowei" last="Chen">Guowei Chen</name><affiliation><nlm:aff id="aff4"><institution>Department of Psychology, Hangzhou Normal University</institution><country>Hangzhou, China</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff5"><institution>Department of Psychology, Zhejiang Normal University</institution><country>Jinhua, China</country></nlm:aff></affiliation></author><author><name sortKey="Ding, Xiao P" sort="Ding, Xiao P" uniqKey="Ding X" first="Xiao P." last="Ding">Xiao P. Ding</name><affiliation><nlm:aff id="aff2"><institution>Department of Applied Psychology and Human Development, Dr. Eric Jackman Institute of Child Study, University of Toronto</institution><country>Toronto, ON, Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff6"><institution>Department of Psychology, National University of Singapore</institution><country>Singapore, Singapore</country></nlm:aff></affiliation></author><author><name sortKey="Fu, Genyue" sort="Fu, Genyue" uniqKey="Fu G" first="Genyue" last="Fu">Genyue Fu</name><affiliation><nlm:aff id="aff4"><institution>Department of Psychology, Hangzhou Normal University</institution><country>Hangzhou, China</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff5"><institution>Department of Psychology, Zhejiang Normal University</institution><country>Jinhua, China</country></nlm:aff></affiliation></author><author><name sortKey="Lee, Kang" sort="Lee, Kang" uniqKey="Lee K" first="Kang" last="Lee">Kang Lee</name><affiliation><nlm:aff id="aff2"><institution>Department of Applied Psychology and Human Development, Dr. Eric Jackman Institute of Child Study, University of Toronto</institution><country>Toronto, ON, Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff5"><institution>Department of Psychology, Zhejiang Normal University</institution><country>Jinhua, China</country></nlm:aff></affiliation></author></analytic><series><title level="j">Frontiers in Neuroscience</title><idno type="ISSN">1662-4548</idno><idno type="eISSN">1662-453X</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Executive function (EF) plays vital roles in our everyday adaptation to the ever-changing environment. However, limited existing studies have linked EF to the resting-state brain activity. The functional connectivity in the resting state between the sub-regions of the brain can reveal the intrinsic neural mechanisms involved in cognitive processing of EF without disturbance from external stimuli. The present study investigated the relations between the behavioral executive function (EF) scores and the resting-state functional network topological properties in the Prefrontal Cortex (PFC). We constructed complex brain functional networks in the PFC from 90 healthy young adults using functional near infrared spectroscopy (fNIRS). We calculated the correlations between the typical network topological properties (regional topological properties and global topological properties) and the scores of both the Total EF and components of EF measured by computer-based Cambridge Neuropsychological Test Automated Battery (CANTAB). We found that the Total EF scores were positively correlated with regional properties in the right dorsal superior frontal gyrus (SFG), whereas the opposite pattern was found in the right triangular inferior frontal gyrus (IFG). Different EF components were related to different regional properties in various PFC areas, such as planning in the right middle frontal gyrus (MFG), working memory mainly in the right MFG and triangular IFG, short-term memory in the left dorsal SFG, and task switch in the right MFG. In contrast, there were no significant findings for global topological properties. Our findings suggested that the PFC plays an important role in individuals' behavioral performance in the executive function tasks. Further, the resting-state functional network can reveal the intrinsic neural mechanisms involved in behavioral EF abilities.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Achard, S" uniqKey="Achard S">S. Achard</name></author><author><name sortKey="Bullmore, E" uniqKey="Bullmore E">E. Bullmore</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Achard, S" uniqKey="Achard S">S. Achard</name></author><author><name sortKey="Salvador, R" uniqKey="Salvador R">R. Salvador</name></author><author><name sortKey="Whitcher, B" uniqKey="Whitcher B">B. Whitcher</name></author><author><name sortKey="Suckling, J" uniqKey="Suckling J">J. Suckling</name></author><author><name sortKey="Bullmore, E D" uniqKey="Bullmore E">E. D. Bullmore</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Aron, A R" uniqKey="Aron A">A. R. Aron</name></author><author><name sortKey="Robbins, T W" uniqKey="Robbins T">T. W. Robbins</name></author><author><name sortKey="Poldrack, R A" uniqKey="Poldrack R">R. A. Poldrack</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Biswal, B" uniqKey="Biswal B">B. Biswal</name></author><author><name sortKey="Zerrin Yetkin, F" uniqKey="Zerrin Yetkin F">F. Zerrin Yetkin</name></author><author><name sortKey="Haughton, V M" uniqKey="Haughton V">V. M. Haughton</name></author><author><name sortKey="Hyde, J S" uniqKey="Hyde J">J. S. Hyde</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Boas, D A" uniqKey="Boas D">D. A. Boas</name></author><author><name sortKey="Elwell, C E" uniqKey="Elwell C">C. E. Elwell</name></author><author><name sortKey="Ferrari, M" uniqKey="Ferrari M">M. Ferrari</name></author><author><name sortKey="Taga, G" uniqKey="Taga G">G. Taga</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bonni, S" uniqKey="Bonni S">S. Bonnì</name></author><author><name sortKey="Perri, R" uniqKey="Perri R">R. Perri</name></author><author><name sortKey="Fadda, L" uniqKey="Fadda L">L. Fadda</name></author><author><name sortKey="Tomaiuolo, F" uniqKey="Tomaiuolo F">F. Tomaiuolo</name></author><author><name sortKey="Koch, G" uniqKey="Koch G">G. Koch</name></author><author><name sortKey="Caltagirone, C" uniqKey="Caltagirone C">C. Caltagirone</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Burgess, P W" uniqKey="Burgess P">P. W. Burgess</name></author><author><name sortKey="Veitch, E" uniqKey="Veitch E">E. Veitch</name></author><author><name sortKey="De Lacy Costello, A" uniqKey="De Lacy Costello A">A. de Lacy Costello</name></author><author><name sortKey="Shallice, T" uniqKey="Shallice T">T. Shallice</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Collette, F" uniqKey="Collette F">F. Collette</name></author><author><name sortKey="Hogge, M" uniqKey="Hogge M">M. Hogge</name></author><author><name sortKey="Salmon, E" uniqKey="Salmon E">E. Salmon</name></author><author><name sortKey="Van Der Linden, M" uniqKey="Van Der Linden M">M. Van der Linden</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Collinson, S L" uniqKey="Collinson S">S. L. Collinson</name></author><author><name sortKey="Tong, S J" uniqKey="Tong S">S. J. Tong</name></author><author><name sortKey="Loh, S S" uniqKey="Loh S">S. S. Loh</name></author><author><name sortKey="Chionh, S B" uniqKey="Chionh S">S. B. Chionh</name></author><author><name sortKey="Merchant, R A" uniqKey="Merchant R">R. A. Merchant</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cui, X" uniqKey="Cui X">X. Cui</name></author><author><name sortKey="Bray, S" uniqKey="Bray S">S. Bray</name></author><author><name sortKey="Bryant, D M" uniqKey="Bryant D">D. M. Bryant</name></author><author><name sortKey="Glover, G H" uniqKey="Glover G">G. H. Glover</name></author><author><name sortKey="Reiss, A L" uniqKey="Reiss A">A. L. Reiss</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Damoiseaux, J S" uniqKey="Damoiseaux J">J. S. Damoiseaux</name></author><author><name sortKey="Beckmann, C F" uniqKey="Beckmann C">C. F. Beckmann</name></author><author><name sortKey="Arigita, E S" uniqKey="Arigita E">E. S. Arigita</name></author><author><name sortKey="Barkhof, F" uniqKey="Barkhof F">F. Barkhof</name></author><author><name sortKey="Scheltens, P" uniqKey="Scheltens P">P. Scheltens</name></author><author><name sortKey="Stam, C J" uniqKey="Stam C">C. J. Stam</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Davidson, R J" uniqKey="Davidson R">R. J. Davidson</name></author><author><name sortKey="Abercrombie, H" uniqKey="Abercrombie H">H. Abercrombie</name></author><author><name sortKey="Nitschke, J B" uniqKey="Nitschke J">J. B. Nitschke</name></author><author><name sortKey="Putnam, K" uniqKey="Putnam K">K. Putnam</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="De Luca, C R" uniqKey="De Luca C">C. R. De Luca</name></author><author><name sortKey="Wood, S J" uniqKey="Wood S">S. J. Wood</name></author><author><name sortKey="Anderson, V" uniqKey="Anderson V">V. Anderson</name></author><author><name sortKey="Buchanan, J A" uniqKey="Buchanan J">J. A. Buchanan</name></author><author><name sortKey="Proffitt, T M" uniqKey="Proffitt T">T. M. Proffitt</name></author><author><name sortKey="Mahony, K" uniqKey="Mahony K">K. Mahony</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="De Vries, F E" uniqKey="De Vries F">F. E. de Vries</name></author><author><name sortKey="De Wit, S J" uniqKey="De Wit S">S. J. de Wit</name></author><author><name sortKey="Cath, D C" uniqKey="Cath D">D. C. Cath</name></author><author><name sortKey="Van Der Werf, Y D" uniqKey="Van Der Werf Y">Y. D. van der Werf</name></author><author><name sortKey="Van Der Borden, V" uniqKey="Van Der Borden V">V. van der Borden</name></author><author><name sortKey="Van Rossum, T B" uniqKey="Van Rossum T">T. B. van Rossum</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ding, X P" uniqKey="Ding X">X. P. Ding</name></author><author><name sortKey="Gao, X" uniqKey="Gao X">X. Gao</name></author><author><name sortKey="Fu, G" uniqKey="Fu G">G. Fu</name></author><author><name sortKey="Lee, K" uniqKey="Lee K">K. Lee</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Douw, L" uniqKey="Douw L">L. Douw</name></author><author><name sortKey="Schoonheim, M M" uniqKey="Schoonheim M">M. M. Schoonheim</name></author><author><name sortKey="Landi, D" uniqKey="Landi D">D. Landi</name></author><author><name sortKey="Van Der Meer, M L" uniqKey="Van Der Meer M">M. L. van der Meer</name></author><author><name sortKey="Geurts, J J" uniqKey="Geurts J">J. J. Geurts</name></author><author><name sortKey="Reijneveld, J C" uniqKey="Reijneveld J">J. C. Reijneveld</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Duan, X" uniqKey="Duan X">X. Duan</name></author><author><name sortKey="Wei, S" uniqKey="Wei S">S. Wei</name></author><author><name sortKey="Wang, G" uniqKey="Wang G">G. Wang</name></author><author><name sortKey="Shi, J" uniqKey="Shi J">J. Shi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ferrari, M" uniqKey="Ferrari M">M. Ferrari</name></author><author><name sortKey="Quaresima, V" uniqKey="Quaresima V">V. Quaresima</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fox, M D" uniqKey="Fox M">M. D. Fox</name></author><author><name sortKey="Snyder, A Z" uniqKey="Snyder A">A. Z. Snyder</name></author><author><name sortKey="Vincent, J L" uniqKey="Vincent J">J. L. Vincent</name></author><author><name sortKey="Corbetta, M" uniqKey="Corbetta M">M. Corbetta</name></author><author><name sortKey="Van Essen, D C" uniqKey="Van Essen D">D. C. Van Essen</name></author><author><name sortKey="Raichle, M E" uniqKey="Raichle M">M. E. Raichle</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Freeman, L C" uniqKey="Freeman L">L. C. Freeman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Friedman, N P" uniqKey="Friedman N">N. P. Friedman</name></author><author><name sortKey="Miyake, A" uniqKey="Miyake A">A. Miyake</name></author><author><name sortKey="Corley, R P" uniqKey="Corley R">R. P. Corley</name></author><author><name sortKey="Young, S E" uniqKey="Young S">S. E. Young</name></author><author><name sortKey="Defries, J C" uniqKey="Defries J">J. C. Defries</name></author><author><name sortKey="Hewitt, J K" uniqKey="Hewitt J">J. K. Hewitt</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Friston, K J" uniqKey="Friston K">K. J. Friston</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Friston, K J" uniqKey="Friston K">K. J. Friston</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gao, Q" uniqKey="Gao Q">Q. Gao</name></author><author><name sortKey="Xu, Q" uniqKey="Xu Q">Q. Xu</name></author><author><name sortKey="Duan, X" uniqKey="Duan X">X. Duan</name></author><author><name sortKey="Liao, W" uniqKey="Liao W">W. Liao</name></author><author><name sortKey="Ding, J" uniqKey="Ding J">J. Ding</name></author><author><name sortKey="Zhang, Z" uniqKey="Zhang Z">Z. Zhang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gau, S S F" uniqKey="Gau S">S. S. F. Gau</name></author><author><name sortKey="Shang, C Y" uniqKey="Shang C">C. Y. Shang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gu, B M" uniqKey="Gu B">B. M. Gu</name></author><author><name sortKey="Park, J Y" uniqKey="Park J">J. Y. Park</name></author><author><name sortKey="Kang, D H" uniqKey="Kang D">D. H. Kang</name></author><author><name sortKey="Lee, S J" uniqKey="Lee S">S. J. Lee</name></author><author><name sortKey="Yoo, S Y" uniqKey="Yoo S">S. Y. Yoo</name></author><author><name sortKey="Jo, H J" uniqKey="Jo H">H. J. Jo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Homae, F" uniqKey="Homae F">F. Homae</name></author><author><name sortKey="Watanabe, H" uniqKey="Watanabe H">H. Watanabe</name></author><author><name sortKey="Nakano, T" uniqKey="Nakano T">T. Nakano</name></author><author><name sortKey="Taga, G" uniqKey="Taga G">G. Taga</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jazbec, S" uniqKey="Jazbec S">S. Jazbec</name></author><author><name sortKey="Pantelis, C" uniqKey="Pantelis C">C. Pantelis</name></author><author><name sortKey="Robbins, T" uniqKey="Robbins T">T. Robbins</name></author><author><name sortKey="Weickert, T" uniqKey="Weickert T">T. Weickert</name></author><author><name sortKey="Weinberger, D R" uniqKey="Weinberger D">D. R. Weinberger</name></author><author><name sortKey="Goldberg, T E" uniqKey="Goldberg T">T. E. Goldberg</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Langer, N" uniqKey="Langer N">N. Langer</name></author><author><name sortKey="Pedroni, A" uniqKey="Pedroni A">A. Pedroni</name></author><author><name sortKey="Gianotti, L R" uniqKey="Gianotti L">L. R. Gianotti</name></author><author><name sortKey="H Nggi, J" uniqKey="H Nggi J">J. Hänggi</name></author><author><name sortKey="Knoch, D" uniqKey="Knoch D">D. Knoch</name></author><author><name sortKey="J Ncke, L" uniqKey="J Ncke L">L. Jäncke</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Latora, V" uniqKey="Latora V">V. Latora</name></author><author><name sortKey="Marchiori, M" uniqKey="Marchiori M">M. Marchiori</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lee, M H" uniqKey="Lee M">M. H. Lee</name></author><author><name sortKey="Smyser, C D" uniqKey="Smyser C">C. D. Smyser</name></author><author><name sortKey="Shimony, J S" uniqKey="Shimony J">J. S. Shimony</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Li, Y" uniqKey="Li Y">Y. Li</name></author><author><name sortKey="Liu, Y" uniqKey="Liu Y">Y. Liu</name></author><author><name sortKey="Li, J" uniqKey="Li J">J. Li</name></author><author><name sortKey="Qin, W" uniqKey="Qin W">W. Qin</name></author><author><name sortKey="Li, K" uniqKey="Li K">K. Li</name></author><author><name sortKey="Yu, C" uniqKey="Yu C">C. Yu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Li, Z" uniqKey="Li Z">Z. Li</name></author><author><name sortKey="Liu, H" uniqKey="Liu H">H. Liu</name></author><author><name sortKey="Liao, X" uniqKey="Liao X">X. Liao</name></author><author><name sortKey="Xu, J" uniqKey="Xu J">J. Xu</name></author><author><name sortKey="Liu, W" uniqKey="Liu W">W. Liu</name></author><author><name sortKey="Tian, F" uniqKey="Tian F">F. Tian</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lin, H Y" uniqKey="Lin H">H. Y. Lin</name></author><author><name sortKey="Tseng, W Y I" uniqKey="Tseng W">W. Y. I. Tseng</name></author><author><name sortKey="Lai, M C" uniqKey="Lai M">M. C. Lai</name></author><author><name sortKey="Matsuo, K" uniqKey="Matsuo K">K. Matsuo</name></author><author><name sortKey="Gau, S S F" uniqKey="Gau S">S. S. F. Gau</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lu, C M" uniqKey="Lu C">C. M. Lu</name></author><author><name sortKey="Zhang, Y J" uniqKey="Zhang Y">Y. J. Zhang</name></author><author><name sortKey="Biswal, B B" uniqKey="Biswal B">B. B. Biswal</name></author><author><name sortKey="Zang, Y F" uniqKey="Zang Y">Y. F. Zang</name></author><author><name sortKey="Peng, D L" uniqKey="Peng D">D. L. Peng</name></author><author><name sortKey="Zhu, C Z" uniqKey="Zhu C">C. Z. Zhu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Maslov, S" uniqKey="Maslov S">S. Maslov</name></author><author><name sortKey="Sneppen, K" uniqKey="Sneppen K">K. Sneppen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Melloni, M" uniqKey="Melloni M">M. Melloni</name></author><author><name sortKey="Urbistondo, C" uniqKey="Urbistondo C">C. Urbistondo</name></author><author><name sortKey="Sede O, L" uniqKey="Sede O L">L. Sedeño</name></author><author><name sortKey="Gelormini, C" uniqKey="Gelormini C">C. Gelormini</name></author><author><name sortKey="Kichic, R" uniqKey="Kichic R">R. Kichic</name></author><author><name sortKey="Ibanez, A" uniqKey="Ibanez A">A. Ibanez</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Miyasaki, J M" uniqKey="Miyasaki J">J. M. Miyasaki</name></author><author><name sortKey="Shannon, K" uniqKey="Shannon K">K. Shannon</name></author><author><name sortKey="Voon, V" uniqKey="Voon V">V. Voon</name></author><author><name sortKey="Ravina, B" uniqKey="Ravina B">B. Ravina</name></author><author><name sortKey="Kleiner Fisman, G" uniqKey="Kleiner Fisman G">G. Kleiner-Fisman</name></author><author><name sortKey="Anderson, K" uniqKey="Anderson K">K. Anderson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Moriguchi, Y" uniqKey="Moriguchi Y">Y. Moriguchi</name></author><author><name sortKey="Hiraki, K" uniqKey="Hiraki K">K. Hiraki</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Newman, M E J" uniqKey="Newman M">M. E. J. Newman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Newman, S D" uniqKey="Newman S">S. D. Newman</name></author><author><name sortKey="Carpenter, P A" uniqKey="Carpenter P">P. A. Carpenter</name></author><author><name sortKey="Varma, S" uniqKey="Varma S">S. Varma</name></author><author><name sortKey="Just, M A" uniqKey="Just M">M. A. Just</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Niu, H" uniqKey="Niu H">H. Niu</name></author><author><name sortKey="He, Y" uniqKey="He Y">Y. He</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Niu, H" uniqKey="Niu H">H. Niu</name></author><author><name sortKey="Wang, J" uniqKey="Wang J">J. Wang</name></author><author><name sortKey="Zhao, T" uniqKey="Zhao T">T. Zhao</name></author><author><name sortKey="Shu, N" uniqKey="Shu N">N. Shu</name></author><author><name sortKey="He, Y" uniqKey="He Y">Y. He</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Oh, A" uniqKey="Oh A">A. Oh</name></author><author><name sortKey="Vidal, J" uniqKey="Vidal J">J. Vidal</name></author><author><name sortKey="Taylor, M J" uniqKey="Taylor M">M. J. Taylor</name></author><author><name sortKey="Pang, E W" uniqKey="Pang E">E. W. Pang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Owen, A M" uniqKey="Owen A">A. M. Owen</name></author><author><name sortKey="Downes, J J" uniqKey="Downes J">J. J. Downes</name></author><author><name sortKey="Sahakian, B J" uniqKey="Sahakian B">B. J. Sahakian</name></author><author><name sortKey="Polkey, C E" uniqKey="Polkey C">C. E. Polkey</name></author><author><name sortKey="Robbins, T W" uniqKey="Robbins T">T. W. Robbins</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Owen, A M" uniqKey="Owen A">A. M. Owen</name></author><author><name sortKey="Roberts, A C" uniqKey="Roberts A">A. C. Roberts</name></author><author><name sortKey="Polkey, C E" uniqKey="Polkey C">C. E. Polkey</name></author><author><name sortKey="Sahakian, B J" uniqKey="Sahakian B">B. J. Sahakian</name></author><author><name sortKey="Robbins, T W" uniqKey="Robbins T">T. W. Robbins</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ozonoff, S" uniqKey="Ozonoff S">S. Ozonoff</name></author><author><name sortKey="Cook, I" uniqKey="Cook I">I. Cook</name></author><author><name sortKey="Coon, H" uniqKey="Coon H">H. Coon</name></author><author><name sortKey="Dawson, G" uniqKey="Dawson G">G. Dawson</name></author><author><name sortKey="Joseph, R M" uniqKey="Joseph R">R. M. Joseph</name></author><author><name sortKey="Klin, A" uniqKey="Klin A">A. Klin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Peters, F" uniqKey="Peters F">F. Peters</name></author><author><name sortKey="Collette, F" uniqKey="Collette F">F. Collette</name></author><author><name sortKey="Degueldre, C" uniqKey="Degueldre C">C. Degueldre</name></author><author><name sortKey="Sterpenich, V" uniqKey="Sterpenich V">V. Sterpenich</name></author><author><name sortKey="Majerus, S" uniqKey="Majerus S">S. Majerus</name></author><author><name sortKey="Salmon, E" uniqKey="Salmon E">E. Salmon</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Petrides, M" uniqKey="Petrides M">M. Petrides</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pu, S" uniqKey="Pu S">S. Pu</name></author><author><name sortKey="Yamada, T" uniqKey="Yamada T">T. Yamada</name></author><author><name sortKey="Yokoyama, K" uniqKey="Yokoyama K">K. Yokoyama</name></author><author><name sortKey="Matsumura, H" uniqKey="Matsumura H">H. Matsumura</name></author><author><name sortKey="Kobayashi, H" uniqKey="Kobayashi H">H. Kobayashi</name></author><author><name sortKey="Sasaki, N" uniqKey="Sasaki N">N. Sasaki</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Reineberg, A E" uniqKey="Reineberg A">A. E. Reineberg</name></author><author><name sortKey="Andrews Hanna, J R" uniqKey="Andrews Hanna J">J. R. Andrews-Hanna</name></author><author><name sortKey="Depue, B E" uniqKey="Depue B">B. E. Depue</name></author><author><name sortKey="Friedman, N P" uniqKey="Friedman N">N. P. Friedman</name></author><author><name sortKey="Banich, M T" uniqKey="Banich M">M. T. Banich</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Selemon, L D" uniqKey="Selemon L">L. D. Selemon</name></author><author><name sortKey="Rajkowska, G" uniqKey="Rajkowska G">G. Rajkowska</name></author><author><name sortKey="Goldman Rakic, P S" uniqKey="Goldman Rakic P">P. S. Goldman-Rakic</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Singh, A K" uniqKey="Singh A">A. K. Singh</name></author><author><name sortKey="Okamoto, M" uniqKey="Okamoto M">M. Okamoto</name></author><author><name sortKey="Dan, H" uniqKey="Dan H">H. Dan</name></author><author><name sortKey="Jurcak, V" uniqKey="Jurcak V">V. Jurcak</name></author><author><name sortKey="Dan, I" uniqKey="Dan I">I. Dan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Smith, D V" uniqKey="Smith D">D. V. Smith</name></author><author><name sortKey="Gseir, M" uniqKey="Gseir M">M. Gseir</name></author><author><name sortKey="Speer, M E" uniqKey="Speer M">M. E. Speer</name></author><author><name sortKey="Delgado, M R" uniqKey="Delgado M">M. R. Delgado</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Steinbrink, J" uniqKey="Steinbrink J">J. Steinbrink</name></author><author><name sortKey="Villringer, A" uniqKey="Villringer A">A. Villringer</name></author><author><name sortKey="Kempf, F" uniqKey="Kempf F">F. Kempf</name></author><author><name sortKey="Haux, D" uniqKey="Haux D">D. Haux</name></author><author><name sortKey="Boden, S" uniqKey="Boden S">S. Boden</name></author><author><name sortKey="Obrig, H" uniqKey="Obrig H">H. Obrig</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Strangman, G" uniqKey="Strangman G">G. Strangman</name></author><author><name sortKey="Culver, J P" uniqKey="Culver J">J. P. Culver</name></author><author><name sortKey="Thompson, J H" uniqKey="Thompson J">J. H. Thompson</name></author><author><name sortKey="Boas, D A" uniqKey="Boas D">D. A. Boas</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tak, S" uniqKey="Tak S">S. Tak</name></author><author><name sortKey="Ye, J C" uniqKey="Ye J">J. C. Ye</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Testa, R" uniqKey="Testa R">R. Testa</name></author><author><name sortKey="Bennett, P" uniqKey="Bennett P">P. Bennett</name></author><author><name sortKey="Ponsford, J" uniqKey="Ponsford J">J. Ponsford</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tian, L" uniqKey="Tian L">L. Tian</name></author><author><name sortKey="Ma, L" uniqKey="Ma L">L. Ma</name></author><author><name sortKey="Wang, L" uniqKey="Wang L">L. Wang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tian, L" uniqKey="Tian L">L. Tian</name></author><author><name sortKey="Wang, J" uniqKey="Wang J">J. Wang</name></author><author><name sortKey="Yan, C" uniqKey="Yan C">C. Yan</name></author><author><name sortKey="He, Y" uniqKey="He Y">Y. He</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Van Den Heuvel, M P" uniqKey="Van Den Heuvel M">M. P. van den Heuvel</name></author><author><name sortKey="Pol, H E H" uniqKey="Pol H">H. E. H. Pol</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Van Den Heuvel, M P" uniqKey="Van Den Heuvel M">M. P. van den Heuvel</name></author><author><name sortKey="Stam, C J" uniqKey="Stam C">C. J. Stam</name></author><author><name sortKey="Kahn, R S" uniqKey="Kahn R">R. S. Kahn</name></author><author><name sortKey="Pol, H E H" uniqKey="Pol H">H. E. H. Pol</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Vidal, J" uniqKey="Vidal J">J. Vidal</name></author><author><name sortKey="Mills, T" uniqKey="Mills T">T. Mills</name></author><author><name sortKey="Pang, E W" uniqKey="Pang E">E. W. Pang</name></author><author><name sortKey="Taylor, M J" uniqKey="Taylor M">M. J. Taylor</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Villringer, A" uniqKey="Villringer A">A. Villringer</name></author><author><name sortKey="Dirnagl, U" uniqKey="Dirnagl U">U. Dirnagl</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wang, L" uniqKey="Wang L">L. Wang</name></author><author><name sortKey="Song, M" uniqKey="Song M">M. Song</name></author><author><name sortKey="Jiang, T" uniqKey="Jiang T">T. Jiang</name></author><author><name sortKey="Zhang, Y" uniqKey="Zhang Y">Y. Zhang</name></author><author><name sortKey="Yu, C" uniqKey="Yu C">C. Yu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wang, Z" uniqKey="Wang Z">Z. Wang</name></author><author><name sortKey="Liu, J" uniqKey="Liu J">J. Liu</name></author><author><name sortKey="Zhong, N" uniqKey="Zhong N">N. Zhong</name></author><author><name sortKey="Qin, Y" uniqKey="Qin Y">Y. Qin</name></author><author><name sortKey="Zhou, H" uniqKey="Zhou H">H. Zhou</name></author><author><name sortKey="Li, K" uniqKey="Li K">K. Li</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Watts, D J" uniqKey="Watts D">D. J. Watts</name></author><author><name sortKey="Strogatz, S H" uniqKey="Strogatz S">S. H. Strogatz</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Widjaja, E" uniqKey="Widjaja E">E. Widjaja</name></author><author><name sortKey="Zamyadi, M" uniqKey="Zamyadi M">M. Zamyadi</name></author><author><name sortKey="Raybaud, C" uniqKey="Raybaud C">C. Raybaud</name></author><author><name sortKey="Snead, O C" uniqKey="Snead O">O. C. Snead</name></author><author><name sortKey="Smith, M L" uniqKey="Smith M">M. L. Smith</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Woo, B K" uniqKey="Woo B">B. K. Woo</name></author><author><name sortKey="Harwood, D G" uniqKey="Harwood D">D. G. Harwood</name></author><author><name sortKey="Melrose, R J" uniqKey="Melrose R">R. J. Melrose</name></author><author><name sortKey="Mandelkern, M A" uniqKey="Mandelkern M">M. A. Mandelkern</name></author><author><name sortKey="Campa, O M" uniqKey="Campa O">O. M. Campa</name></author><author><name sortKey="Walston, A" uniqKey="Walston A">A. Walston</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wu, L" uniqKey="Wu L">L. Wu</name></author><author><name sortKey="Soder, R B" uniqKey="Soder R">R. B. Soder</name></author><author><name sortKey="Schoemaker, D" uniqKey="Schoemaker D">D. Schoemaker</name></author><author><name sortKey="Carbonnell, F" uniqKey="Carbonnell F">F. Carbonnell</name></author><author><name sortKey="Sziklas, V" uniqKey="Sziklas V">V. Sziklas</name></author><author><name sortKey="Rowley, J" uniqKey="Rowley J">J. Rowley</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Xu, P" uniqKey="Xu P">P. Xu</name></author><author><name sortKey="Huang, R" uniqKey="Huang R">R. Huang</name></author><author><name sortKey="Wang, J" uniqKey="Wang J">J. Wang</name></author><author><name sortKey="Van Dam, N T" uniqKey="Van Dam N">N. T. Van Dam</name></author><author><name sortKey="Xie, T" uniqKey="Xie T">T. Xie</name></author><author><name sortKey="Dong, Z" uniqKey="Dong Z">Z. Dong</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ye, J" uniqKey="Ye J">J. Ye</name></author><author><name sortKey="Tak, S" uniqKey="Tak S">S. Tak</name></author><author><name sortKey="Jang, K" uniqKey="Jang K">K. Jang</name></author><author><name sortKey="Jung, J" uniqKey="Jung J">J. Jung</name></author><author><name sortKey="Jang, J" uniqKey="Jang J">J. Jang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zelazo, P D" uniqKey="Zelazo P">P. D. Zelazo</name></author><author><name sortKey="Muller, U" uniqKey="Muller U">U. Müller</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zou, Q" uniqKey="Zou Q">Q. Zou</name></author><author><name sortKey="Ross, T J" uniqKey="Ross T">T. J. Ross</name></author><author><name sortKey="Gu, H" uniqKey="Gu H">H. Gu</name></author><author><name sortKey="Geng, X" uniqKey="Geng X">X. Geng</name></author><author><name sortKey="Zuo, X N" uniqKey="Zuo X">X. N. Zuo</name></author><author><name sortKey="Hong, L E" uniqKey="Hong L">L. E. Hong</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Front Neurosci</journal-id><journal-id journal-id-type="iso-abbrev">Front Neurosci</journal-id><journal-id journal-id-type="publisher-id">Front. Neurosci.</journal-id><journal-title-group><journal-title>Frontiers in Neuroscience</journal-title></journal-title-group><issn pub-type="ppub">1662-4548</issn><issn pub-type="epub">1662-453X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">27774047</article-id><article-id pub-id-type="pmc">5054000</article-id><article-id pub-id-type="doi">10.3389/fnins.2016.00452</article-id><article-categories><subj-group subj-group-type="heading"><subject>Neuroscience</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Linking Resting-State Networks in the Prefrontal Cortex to Executive Function: A Functional Near Infrared Spectroscopy Study</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Zhao</surname><given-names>Jia</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Liu</surname><given-names>Jiangang</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="author-notes" rid="fn001"><sup>*</sup></xref><uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/190797/overview"></uri></contrib><contrib contrib-type="author"><name><surname>Jiang</surname><given-names>Xin</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author"><name><surname>Zhou</surname><given-names>Guifei</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Chen</surname><given-names>Guowei</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref><xref ref-type="aff" rid="aff5"><sup>5</sup></xref></contrib><contrib contrib-type="author"><name><surname>Ding</surname><given-names>Xiao P.</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="aff" rid="aff6"><sup>6</sup></xref><uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/299793/overview"></uri></contrib><contrib contrib-type="author"><name><surname>Fu</surname><given-names>Genyue</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref><xref ref-type="aff" rid="aff5"><sup>5</sup></xref><xref ref-type="author-notes" rid="fn002"><sup>*</sup></xref><uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/71874/overview"></uri></contrib><contrib contrib-type="author"><name><surname>Lee</surname><given-names>Kang</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="aff" rid="aff5"><sup>5</sup></xref><xref ref-type="author-notes" rid="fn003"><sup>*</sup></xref><uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/11893/overview"></uri></contrib></contrib-group><aff id="aff1"><sup>1</sup><institution>School of Computer and Information Technology, Beijing Jiaotong University</institution><country>Beijing, China</country></aff><aff id="aff2"><sup>2</sup><institution>Department of Applied Psychology and Human Development, Dr. Eric Jackman Institute of Child Study, University of Toronto</institution><country>Toronto, ON, Canada</country></aff><aff id="aff3"><sup>3</sup><institution>Department of Computer Science, University College London</institution><country>London, UK</country></aff><aff id="aff4"><sup>4</sup><institution>Department of Psychology, Hangzhou Normal University</institution><country>Hangzhou, China</country></aff><aff id="aff5"><sup>5</sup><institution>Department of Psychology, Zhejiang Normal University</institution><country>Jinhua, China</country></aff><aff id="aff6"><sup>6</sup><institution>Department of Psychology, National University of Singapore</institution><country>Singapore, Singapore</country></aff><author-notes><fn fn-type="edited-by"><p>Edited by: Bernd Weber, University of Bonn, Germany</p></fn><fn fn-type="edited-by"><p>Reviewed by: David V. Smith, Temple University, USA; Inti Brazil, Radboud University Nijmegen, Netherlands</p></fn><corresp id="fn001">*Correspondence: Jiangang Liu <email xlink:type="simple">liujg@bjtu.edu.cn</email></corresp><corresp id="fn002">Genyue Fu <email xlink:type="simple">fugenyue@hznu.edu.cn</email></corresp><corresp id="fn003">Kang Lee <email xlink:type="simple">kang.lee@utoronto.ca</email></corresp><fn fn-type="other" id="fn004"><p>This article was submitted to Decision Neuroscience, a section of the journal Frontiers in Neuroscience</p></fn></author-notes><pub-date pub-type="epub"><day>07</day><month>10</month><year>2016</year></pub-date><pub-date pub-type="collection"><year>2016</year></pub-date><volume>10</volume><elocation-id>452</elocation-id><history><date date-type="received"><day>29</day><month>3</month><year>2016</year></date><date date-type="accepted"><day>20</day><month>9</month><year>2016</year></date></history><permissions><copyright-statement>Copyright © 2016 Zhao, Liu, Jiang, Zhou, Chen, Ding, Fu and Lee.</copyright-statement><copyright-year>2016</copyright-year><copyright-holder>Zhao, Liu, Jiang, Zhou, Chen, Ding, Fu and Lee</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Executive function (EF) plays vital roles in our everyday adaptation to the ever-changing environment. However, limited existing studies have linked EF to the resting-state brain activity. The functional connectivity in the resting state between the sub-regions of the brain can reveal the intrinsic neural mechanisms involved in cognitive processing of EF without disturbance from external stimuli. The present study investigated the relations between the behavioral executive function (EF) scores and the resting-state functional network topological properties in the Prefrontal Cortex (PFC). We constructed complex brain functional networks in the PFC from 90 healthy young adults using functional near infrared spectroscopy (fNIRS). We calculated the correlations between the typical network topological properties (regional topological properties and global topological properties) and the scores of both the Total EF and components of EF measured by computer-based Cambridge Neuropsychological Test Automated Battery (CANTAB). We found that the Total EF scores were positively correlated with regional properties in the right dorsal superior frontal gyrus (SFG), whereas the opposite pattern was found in the right triangular inferior frontal gyrus (IFG). Different EF components were related to different regional properties in various PFC areas, such as planning in the right middle frontal gyrus (MFG), working memory mainly in the right MFG and triangular IFG, short-term memory in the left dorsal SFG, and task switch in the right MFG. In contrast, there were no significant findings for global topological properties. Our findings suggested that the PFC plays an important role in individuals' behavioral performance in the executive function tasks. Further, the resting-state functional network can reveal the intrinsic neural mechanisms involved in behavioral EF abilities.</p></abstract><kwd-group><kwd>resting-state</kwd><kwd>fNIRS</kwd><kwd>small-world</kwd><kwd>executive function</kwd><kwd>prefrontal cortex</kwd></kwd-group><funding-group><award-group><funding-source id="cn001">National Natural Science Foundation of China<named-content content-type="fundref-id">10.13039/501100001809</named-content></funding-source></award-group><award-group><funding-source id="cn002">Natural Sciences and Engineering Research Council of Canada<named-content content-type="fundref-id">10.13039/501100000038</named-content></funding-source></award-group><award-group><funding-source id="cn003">National Institutes of Health<named-content content-type="fundref-id">10.13039/100000002</named-content></funding-source></award-group></funding-group><counts><fig-count count="8"></fig-count><table-count count="3"></table-count><equation-count count="11"></equation-count><ref-count count="74"></ref-count><page-count count="17"></page-count><word-count count="10317"></word-count></counts></article-meta></front><body><sec sec-type="intro" id="s1"><title>Introduction</title><p>Executive function (EF) refers to a set of higher order psychological processes that are involved in goal-oriented behavior (Zelazo and Müller, <xref rid="B73" ref-type="bibr">2002</xref>). It consists of a variety of cognitive components, such as planning, working memory, short-term memory, inhibition, and switch (Zelazo and Müller, <xref rid="B73" ref-type="bibr">2002</xref>; De Luca et al., <xref rid="B13" ref-type="bibr">2003</xref>; Testa et al., <xref rid="B58" ref-type="bibr">2012</xref>). EF plays vital roles in our everyday adaptation to the environment. Executive dysfunction may increase the risk of serious cognitive or social problems, such as attention-deficit hyperactivity disorder (ADHD), autism, and Parkinson's disease (Zelazo and Müller, <xref rid="B73" ref-type="bibr">2002</xref>; Miyasaki et al., <xref rid="B38" ref-type="bibr">2006</xref>).</p><p>Due to EF's significant role in our lives, its neural correlates have been extensively investigated in many neuropsychological, clinical, and neuroimaging studies. Researches have consistently shown that the prefrontal cortex (PFC) is involved in EF. The most direct evidence comes from neuropsychological studies, suggesting that cerebral lesions or damages to the PFC cause deficits in EF (Owen et al., <xref rid="B45" ref-type="bibr">1990</xref>, <xref rid="B46" ref-type="bibr">1991</xref>; Burgess et al., <xref rid="B7" ref-type="bibr">2000</xref>; Bonnì et al., <xref rid="B6" ref-type="bibr">2014</xref>). Clinical studies have also shown that mental illnesses with EF deficits, such as obsessive-compulsive disorder (OCD), depression, and schizophrenia are related to functional impairment of PFC (Selemon et al., <xref rid="B52" ref-type="bibr">1998</xref>; Davidson et al., <xref rid="B12" ref-type="bibr">1999</xref>; Gu et al., <xref rid="B26" ref-type="bibr">2008</xref>; Melloni et al., <xref rid="B37" ref-type="bibr">2012</xref>). Additionally, recent neuroimaging studies using a variety of non-invasive functional neuroimaging methodologies, such as functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and functional near-infrared spectroscopy (fNIRS), have revealed significant relations between EF performance and neural activation levels in the PFC in both healthy and clinical individuals (Peters et al., <xref rid="B48" ref-type="bibr">2009</xref>; Pu et al., <xref rid="B50" ref-type="bibr">2011</xref>; Melloni et al., <xref rid="B37" ref-type="bibr">2012</xref>; Moriguchi and Hiraki, <xref rid="B39" ref-type="bibr">2013</xref>; de Vries et al., <xref rid="B14" ref-type="bibr">2014</xref>; Oh et al., <xref rid="B44" ref-type="bibr">2014</xref>). For example, for healthy participants, fMRI activation in the bilateral PFC was associated with a planning task (Newman et al., <xref rid="B41" ref-type="bibr">2003</xref>), and the neural MEG activities can be enhanced by a set-shifting task (Oh et al., <xref rid="B44" ref-type="bibr">2014</xref>). For patients with some mental illnesses (e.g., Alzheimer's disease or depression), the decreased neural activities of the PFC was related to a poor performance of EF tasks (Peters et al., <xref rid="B48" ref-type="bibr">2009</xref>; Pu et al., <xref rid="B50" ref-type="bibr">2011</xref>).</p><p>However, it should be noted that the existing neuroimaging studies mostly focused on the activation of the PFC elicited by EF tasks. Limited existing studies have linked EF to the resting-state brain functional activity. Resting-state functional activity refers to the internally spontaneous fluctuations of the brain in a natural condition without stimulation (Biswal et al., <xref rid="B4" ref-type="bibr">1995</xref>). It has been consistently shown that functional connectivity in the resting state between the sub-regions of our brains can reveal the intrinsic neural mechanisms involved in cognitive processing without disturbance from external stimuli (Fox et al., <xref rid="B19" ref-type="bibr">2005</xref>).</p><p>The human brain is a dynamical system that is characterized by complex exchanges of information among the brain regions (Friston, <xref rid="B22" ref-type="bibr">1994</xref>). Such dynamic interaction and synergy of multiple brain regions is necessary for high level cognitive processing, especially executive function (Douw et al., <xref rid="B16" ref-type="bibr">2011</xref>). Indeed, some recent studies have found significant relations between high level cognitive processing and functional connectivity. For example, Wang et al. (<xref rid="B65" ref-type="bibr">2011</xref>) found that the level of intelligence was correlated with resting-state connectivity of multiple brain regions including the middle frontal, inferior parietal lobules. Douw et al. (<xref rid="B16" ref-type="bibr">2011</xref>) using MEG reported significant correlations between the overall brain resting-state topology and performance in cognitive tasks measuring EF, attention, and working memory. Additional studies have also demonstrated that resting-state functional connectivity abnormality may be associated with cognitive decline (Damoiseaux et al., <xref rid="B11" ref-type="bibr">2008</xref>; Douw et al., <xref rid="B16" ref-type="bibr">2011</xref>; Wang et al., <xref rid="B65" ref-type="bibr">2011</xref>). Most relevant to the present study, Reineberg et al. (<xref rid="B51" ref-type="bibr">2015</xref>) found that EF was associated with functional connectivity intensity of the right frontoparietal resting-state functional network identified by independent components analysis (ICA). This finding suggested that the resting-state functional connectivity analysis method can be applied to the study of neural correlates of EF.</p><p>The present study used resting-state high-density fNIRS to examine the relations between behavioral performances in different EF tasks and functional connectivity of the PFC. fNIRS is a non-invasive neuroimaging methodology that measures cortical hemodynamic changes optically (Villringer and Dirnagl, <xref rid="B64" ref-type="bibr">1994</xref>; Tak and Ye, <xref rid="B57" ref-type="bibr">2014</xref>). It has many advantages over fMRI (Ding et al., <xref rid="B15" ref-type="bibr">2013</xref>; Tak and Ye, <xref rid="B57" ref-type="bibr">2014</xref>) which are particularly important for the present study. Over the past decade, the appropriateness of using fNIRS to examine localized cortical neural activities has been well established (Ferrari and Quaresima, <xref rid="B18" ref-type="bibr">2012</xref>; for reviews, see Boas et al., <xref rid="B5" ref-type="bibr">2014</xref>). In particular, systematic studies with human adults using both fNIRS and fMRI have produced highly consistent results regarding the cortical locations of specific cognitive functions (for a review, see Cui et al., <xref rid="B10" ref-type="bibr">2011</xref>). Although the spatial resolution of NIRS is worse than MRI (cm vs. mm), the temporal resolution of fNIRS is greater than that of fMRI (e.g., 10 vs. 0.5 Hz). The higher temporal resolution of fNIRS is helpful to describe the time course of the fluctuations of the brain's neural activities more precisely. More specifically, the high temporal resolution of the fNIRS signals allows for preventing the higher frequency physiological signals from interfering with low-frequency fluctuations (Lu et al., <xref rid="B35" ref-type="bibr">2010</xref>), which is the focus of our study. Additionally, the operating cost and complexity of a NIRS system is far less than a MRI machine, allowing for the collection of data from a larger sample of participants to ensure a high level of statistical power. The advantages of fNIRS over fMRI lend fNIRS very well as a tool to study functional connectivity.</p><p>The applications of resting-state fMRI have been widely accepted (for a review, see Lee et al., <xref rid="B31" ref-type="bibr">2013</xref>). Given the similarity between fNIRS and fMRI hemodynamic signals (Steinbrink et al., <xref rid="B55" ref-type="bibr">2006</xref>), it is reasonable to extend the techniques of resting-state study to fNIRS. Over the past several years, there were an increasing number of studies on the resting-state functional connectivity using fNIRS (for a review, see Niu and He, <xref rid="B42" ref-type="bibr">2014</xref>). For example, Lu et al. (<xref rid="B35" ref-type="bibr">2010</xref>) obtained the resting-state fNIRS functional connectivity maps using both seed-based correlation analysis and data-driven cluster analysis. Niu et al. (<xref rid="B43" ref-type="bibr">2012</xref>) investigated the topological organization of the brain functional networks based on fNIRS, and found that the topological properties were consistent with previous fMRI findings. Recently, Li et al. (<xref rid="B33" ref-type="bibr">2015</xref>) capitalized on fNIRS' higher temporal resolution and examined the dynamic characteristics of the resting-state functional connectivity on the whole cortex by a sliding-window correlation method. They revealed high temporal variabilities in the cortical resting-state functional connectivity. In summary, the feasibility and validity of fNIRS for the assessment of resting-state neural activities had been sufficiently established.</p><p>The present study capitalized on the advantages of fNIRS to obtain resting-state functional data from 90 adults. We then used graph theory to analyze the resting-state data. The analysis of the complex brain networks based on graph theory is one of the most widely used methods in resting-state functional connectivity analysis (for a review, see van den Heuvel and Pol, <xref rid="B61" ref-type="bibr">2010</xref>). This is because graph theory provides the state-of-the-art measures to describe the interaction and synergy of information between the brain regions from a global perspective. In the present study, we used this method to link the network properties of the functional connectivity among the regions of the PFC to behavioral EF performances.</p><p>Although some studies have linked the resting-state functional connectivity to performances in various EF tasks (Widjaja et al., <xref rid="B68" ref-type="bibr">2013</xref>; Lin et al., <xref rid="B34" ref-type="bibr">2015</xref>; Reineberg et al., <xref rid="B51" ref-type="bibr">2015</xref>), to the best of our knowledge, few have specifically focused on the resting-state network topological properties. One exception was an MEG study by Douw et al. (<xref rid="B16" ref-type="bibr">2011</xref>). They, however, only focused on global parameters of resting-state network and did not analyze regional parameters. Thus, it is entirely unclear whether and to what extent the nodal and global topological properties of the brain network were associated with behavioral performances in various EF tasks. The present study aimed to bridge this significant gap in the literature.</p><p>More specifically, we aimed to examine an important theoretical question that hitherto has yet to be answered empirically: Whether and to what extent are global or local neural network connectivities associated with executive functioning in behavior? One possibility is that both types of connectivities are important to engender EF behaviors and therefore the greater people's global and local functioning connectivities, the greater their behavioral EF performance (the Global and Local Property Hypothesis). Although no specific evidence exists to support this possibility, a recent study showed that the resting-state global neural connectivity indexes are predictive of participants' intelligence as measured by IQ (Langer et al., <xref rid="B29" ref-type="bibr">2012</xref>). Given some components of EF (e.g., working memory) is part of an intelligence scale that measures IQ, we hypothesized that global connectivity indexes are related to EF performance. Further, given the roles of the PFC in EF processing revealed by the existing studies, we hypothesized that the local topological properties of the resting-state brain functional network in the PFC would be significantly correlated with participants' overall EF performance. In addition, given the fact that different EF components (e.g., working memory, switch) entails different cognitive processes with different underlying neural mechanisms, we hypothesized that participants' scores in specific EF components would be related to different regional properties and such linkage should emerge in different regions in the PFC.</p><p>However, it is well established in the behavioral literature that IQ is an index of a general and global intellectual ability (van den Heuvel et al., <xref rid="B62" ref-type="bibr">2009</xref>; Langer et al., <xref rid="B29" ref-type="bibr">2012</xref>), whereas EF is a specialized cognitive ability (though with wide-range usages in a variety of situations). Thus, an alternative possibility is that unlike IQ, EF might be more associated with local than global neural connectivities (the Local Property Only Hypothesis). If this possibility is true, the local, but not global, network topological properties would be expected to link to both overall EF behavioral performance and different EF components.</p></sec><sec sec-type="materials and methods" id="s2"><title>Materials and methods</title><sec><title>Participants</title><p>Ninety healthy right-handed young adults (30 males; 20.4 ± 1.5 years old) with normal or corrected to normal vision participated in the present study. None of them had any history of learning disabilities, neurological and psychiatric disorders. All participants gave informed written consent prior to their participation. This research was approved by the Ethics Committee of Zhejiang Normal University.</p></sec><sec><title>Imaging acquisitions and data preprocessing</title><p>During the resting state, participants were required to sit still with eyes closed but not fall asleep, and to think of nothing as far as possible. The NIRS data collection lasted 12 min for each participant.</p><p>A 24 channel continuous wave system (ETG-4000 Hitachi Medical Co., Japan) was used for resting-state fNIRS data acquisition. The instrument consisted of five light emitters (each generated two wavelengths of near-infrared light: 760 and 850 nm) and four detectors on each hemisphere which allowed for 24 different measurement channels. During the experiment, the probes were embedded in two rubber shells, which were covered with a swimming cap to keep it attached to the participant's head. The inter-optode distance was 30 mm and the sampling rate was set to 10 Hz. The measurement of neural activities approximately 15–25 mm beneath the scalp was achieved.</p><p>A 3D digitizer (EZT-DM401, Hitachi Medical Corporation, Japan) was used to complete the 3 dimensional spatial registration of NIRS channel locations. The estimated corresponding location of each NIRS channel in the Montreal Neurological Institute (MNI) space was obtained using the probabilistic registration method (Singh et al., <xref rid="B53" ref-type="bibr">2005</xref>). The NIRS channels covered the bilateral PFC, including the dorsal superior frontal gyrus (SFG), the middle frontal gyrus (MFG), the triangular inferior frontal gyrus (IFG) and the left orbital MFG (Brodmann's Areas 9, 10, 45, 46) (Figure <xref ref-type="fig" rid="F1">1</xref>). The Brodmann's Areas (MRIcro), anatomical label and MNI coordinates were listed in Table <xref ref-type="table" rid="T1">1</xref>.</p><fig id="F1" position="float"><label>Figure 1</label><caption><p><bold>Locations of near-infrared spectroscopy (NIRS) channels</bold>. <bold>(A)</bold> The estimated positions of the NIRS channels. <bold>(B)</bold> The anatomical label (color) and Brodmann's Areas (MRIcro) (curve). Abbreviation: SFG (dor), dorsal superior frontal gyrus; MFG, middle frontal gyrus; ORBmed, Orbitofrontal cortex (medial); IFG (triang), triangular inferior frontal gyrus.</p></caption><graphic xlink:href="fnins-10-00452-g0001"></graphic></fig><table-wrap id="T1" position="float"><label>Table 1</label><caption><p><bold>The Brodmann's Areas (BA), anatomical label (AAL), and MNI coordinates of near-infrared spectroscopy (NIRS) channels</bold>.</p></caption><table frame="hsides" rules="groups"><thead><tr><th valign="top" align="left" rowspan="1" colspan="1"><bold>Channel</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>BA</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>AAL</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>MNI (x, y, z)</bold></th></tr></thead><tbody><tr><td valign="top" align="left" rowspan="1" colspan="1">1</td><td valign="top" align="center" rowspan="1" colspan="1">46</td><td valign="top" align="left" rowspan="1" colspan="1">ORBmed</td><td valign="top" align="center" rowspan="1" colspan="1">−46, 54, −3</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">2</td><td valign="top" align="center" rowspan="1" colspan="1">10</td><td valign="top" align="left" rowspan="1" colspan="1">SFG (dor)</td><td valign="top" align="center" rowspan="1" colspan="1">−26, 68, 3</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">3</td><td valign="top" align="center" rowspan="1" colspan="1">45</td><td valign="top" align="left" rowspan="1" colspan="1">IFG (triang)</td><td valign="top" align="center" rowspan="1" colspan="1">−53, 41, 5</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">4</td><td valign="top" align="center" rowspan="1" colspan="1">10</td><td valign="top" align="left" rowspan="1" colspan="1">MFG</td><td valign="top" align="center" rowspan="1" colspan="1">−38, 60, 12</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">5</td><td valign="top" align="center" rowspan="1" colspan="1">10</td><td valign="top" align="left" rowspan="1" colspan="1">SFG (dor)</td><td valign="top" align="center" rowspan="1" colspan="1">−17, 71, 17</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">6</td><td valign="top" align="center" rowspan="1" colspan="1">45</td><td valign="top" align="left" rowspan="1" colspan="1">MFG</td><td valign="top" align="center" rowspan="1" colspan="1">−47, 46, 21</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">7</td><td valign="top" align="center" rowspan="1" colspan="1">46</td><td valign="top" align="left" rowspan="1" colspan="1">SFG (dor)</td><td valign="top" align="center" rowspan="1" colspan="1">−27, 61, 26</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">8</td><td valign="top" align="center" rowspan="1" colspan="1">45</td><td valign="top" align="left" rowspan="1" colspan="1">IFG (triang)</td><td valign="top" align="center" rowspan="1" colspan="1">−52, 29, 28</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">9</td><td valign="top" align="center" rowspan="1" colspan="1">46</td><td valign="top" align="left" rowspan="1" colspan="1">MFG</td><td valign="top" align="center" rowspan="1" colspan="1">−37, 47, 34</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">10</td><td valign="top" align="center" rowspan="1" colspan="1">9</td><td valign="top" align="left" rowspan="1" colspan="1">SFG (dor)</td><td valign="top" align="center" rowspan="1" colspan="1">−14, 58, 39</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">11</td><td valign="top" align="center" rowspan="1" colspan="1">9</td><td valign="top" align="left" rowspan="1" colspan="1">MFG</td><td valign="top" align="center" rowspan="1" colspan="1">−44, 31, 43</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">12</td><td valign="top" align="center" rowspan="1" colspan="1">9</td><td valign="top" align="left" rowspan="1" colspan="1">SFG (dor)</td><td valign="top" align="center" rowspan="1" colspan="1">−23, 45, 46</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">13</td><td valign="top" align="center" rowspan="1" colspan="1">10</td><td valign="top" align="left" rowspan="1" colspan="1">SFG (dor)</td><td valign="top" align="center" rowspan="1" colspan="1">27, 70, 4</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">14</td><td valign="top" align="center" rowspan="1" colspan="1">46</td><td valign="top" align="left" rowspan="1" colspan="1">IFG (triang)</td><td valign="top" align="center" rowspan="1" colspan="1">46, 58, −2</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">15</td><td valign="top" align="center" rowspan="1" colspan="1">10</td><td valign="top" align="left" rowspan="1" colspan="1">SFG (dor)</td><td valign="top" align="center" rowspan="1" colspan="1">18, 71, 18</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">16</td><td valign="top" align="center" rowspan="1" colspan="1">10</td><td valign="top" align="left" rowspan="1" colspan="1">MFG</td><td valign="top" align="center" rowspan="1" colspan="1">39, 63, 12</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">17</td><td valign="top" align="center" rowspan="1" colspan="1">45</td><td valign="top" align="left" rowspan="1" colspan="1">IFG (triang)</td><td valign="top" align="center" rowspan="1" colspan="1">54, 44, 6</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">18</td><td valign="top" align="center" rowspan="1" colspan="1">10</td><td valign="top" align="left" rowspan="1" colspan="1">MFG</td><td valign="top" align="center" rowspan="1" colspan="1">28, 63, 26</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">19</td><td valign="top" align="center" rowspan="1" colspan="1">46</td><td valign="top" align="left" rowspan="1" colspan="1">MFG</td><td valign="top" align="center" rowspan="1" colspan="1">47, 50, 21</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">20</td><td valign="top" align="center" rowspan="1" colspan="1">9</td><td valign="top" align="left" rowspan="1" colspan="1">SFG (dor)</td><td valign="top" align="center" rowspan="1" colspan="1">16, 60, 37</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">21</td><td valign="top" align="center" rowspan="1" colspan="1">46</td><td valign="top" align="left" rowspan="1" colspan="1">MFG</td><td valign="top" align="center" rowspan="1" colspan="1">36, 51, 34</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">22</td><td valign="top" align="center" rowspan="1" colspan="1">45</td><td valign="top" align="left" rowspan="1" colspan="1">IFG (triang)</td><td valign="top" align="center" rowspan="1" colspan="1">54, 33, 29</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">23</td><td valign="top" align="center" rowspan="1" colspan="1">9</td><td valign="top" align="left" rowspan="1" colspan="1">SFG (dor)</td><td valign="top" align="center" rowspan="1" colspan="1">25, 48, 46</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">24</td><td valign="top" align="center" rowspan="1" colspan="1">9</td><td valign="top" align="left" rowspan="1" colspan="1">MFG</td><td valign="top" align="center" rowspan="1" colspan="1">44, 35, 43</td></tr></tbody></table><table-wrap-foot><p><italic>SFG (dor), dorsal superior frontal gyrus; MFG, middle frontal gyrus; ORBmed, Orbitofrontal cortex (medial); IFG (triang), triangular inferior frontal gyrus</italic>.</p></table-wrap-foot></table-wrap><p>The raw optical signal was firstly converted to hemoglobin signal using the modified Beer-Lambert Law conducted by NIRS-SPM (Ye et al., <xref rid="B72" ref-type="bibr">2009</xref>). To obtain relatively steady signals, the first 2-min of data for each participant were discarded. Preprocessing was conducted by the resting-state fMRI data analysis toolkit (REST) (<ext-link ext-link-type="uri" xlink:href="http://resting-fmri.sourceforge.net">http://resting-fmri.sourceforge.net</ext-link>). Briefly, after removing the linear trend, in order to reduce low-frequency drift and high-frequency physiological noise, the hemoglobin data were passed through a band-pass filter (0.009–0.08 Hz) which was consistent with previous fNIRS study (Niu et al., <xref rid="B43" ref-type="bibr">2012</xref>). Although both oxygenated hemoglobin ([oxy-Hb]) and deoxygenated hemoglobin ([deoxy-Hb]) signals were obtained in this study, we only chose [oxy-Hb] data to perform further analyses due to its superior signal-to-noise ratio relative to [deoxy-Hb] (Strangman et al., <xref rid="B56" ref-type="bibr">2002</xref>; Homae et al., <xref rid="B27" ref-type="bibr">2007</xref>).</p></sec><sec><title>Behavioral tasks description</title><p>After the image acquisitions, the participants were measured EF behaviorally using the computer-based Cambridge Neuropsychological Test Automated Battery (CANTAB). CANTAB is a widely used and well validated cognitive test software system that provides an automated and efficient assessment of multiple EF components (<ext-link ext-link-type="uri" xlink:href="http://www.cambridgecognition.com">http://www.cambridgecognition.com</ext-link>). Due to its normative procedures and nonverbal nature, CANTAB is highly suitable for use in different cultural settings (De Luca et al., <xref rid="B13" ref-type="bibr">2003</xref>; Gau and Shang, <xref rid="B25" ref-type="bibr">2010</xref>). CANTAB had been used extensively to assess cognitive impairments of patients with mental illness (Ozonoff et al., <xref rid="B47" ref-type="bibr">2004</xref>; Gau and Shang, <xref rid="B25" ref-type="bibr">2010</xref>; Collinson et al., <xref rid="B9" ref-type="bibr">2014</xref>), and group differences in EF (De Luca et al., <xref rid="B13" ref-type="bibr">2003</xref>).</p><p>In the present study, four cognitive tests were selected from the CANTAB to measure EF including Stockings of Cambridge (SOC), Spatial Working Memory (SWM), Spatial Span (SSP), and Intra-dimensional/Extra-dimensional Shifts (IED). Before each test, participants were told the rules through a brief oral instruction from the experimenter to ensure an accurate understanding. Participants were tested individually in a quiet room. All tests were performed on a computer screen.</p><p>The SOC was designed to be similar to Tower of London tasks, which assessed the ability of spatial planning and motor control. Two groups of patterns containing three colored balls were displayed on the computer screen in a specific configuration. The participants needed to move the balls on the bottom of screen to match with the goal set on the top using as few moves as possible. The number of moves increased from 2 to 4. In the motor control phases inserted in the test, the software waited for 500 ms and then moved a ball in the example configuration, and the subject needed to follow what it did. The duration after each problem was 3000 ms. The outcome scores of problems solved in minimum moves were chosen to measure planning (Table <xref ref-type="table" rid="T2">2</xref>).</p><table-wrap id="T2" position="float"><label>Table 2</label><caption><p><bold>The scores of EF performances provided by CANTAB</bold>.</p></caption><table frame="hsides" rules="groups"><thead><tr><th valign="top" align="left" rowspan="1" colspan="1"><bold>EF tasks</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Components of EF measurement</bold></th><th valign="top" align="left" rowspan="1" colspan="1"><bold>Outcome measures scores</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>Mean</bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold>SD</bold></th></tr></thead><tbody><tr><td valign="top" align="left" rowspan="1" colspan="1">SOC</td><td valign="top" align="left" rowspan="1" colspan="1">planning</td><td valign="top" align="left" rowspan="1" colspan="1">Problems solved in minimum moves</td><td valign="top" align="char" char="." rowspan="1" colspan="1">−1.26</td><td valign="top" align="char" char="." rowspan="1" colspan="1">1.07</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">SWM</td><td valign="top" align="left" rowspan="1" colspan="1">working memory</td><td valign="top" align="left" rowspan="1" colspan="1">Between errors</td><td valign="top" align="char" char="." rowspan="1" colspan="1">−0.58</td><td valign="top" align="char" char="." rowspan="1" colspan="1">1.34</td></tr><tr><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">Strategy</td><td valign="top" align="char" char="." rowspan="1" colspan="1">−0.53</td><td valign="top" align="char" char="." rowspan="1" colspan="1">0.92</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">SSP</td><td valign="top" align="left" rowspan="1" colspan="1">short−term memory</td><td valign="top" align="left" rowspan="1" colspan="1">Span length</td><td valign="top" align="char" char="." rowspan="1" colspan="1">0.54</td><td valign="top" align="char" char="." rowspan="1" colspan="1">1.11</td></tr><tr><td rowspan="1" colspan="1"></td><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">Total errors</td><td valign="top" align="char" char="." rowspan="1" colspan="1">−0.14</td><td valign="top" align="char" char="." rowspan="1" colspan="1">1.34</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">IED</td><td valign="top" align="left" rowspan="1" colspan="1">inhibition</td><td valign="top" align="left" rowspan="1" colspan="1">Pre−ED errors</td><td valign="top" align="char" char="." rowspan="1" colspan="1">0.05</td><td valign="top" align="char" char="." rowspan="1" colspan="1">0.59</td></tr><tr><td rowspan="1" colspan="1"></td><td valign="top" align="left" rowspan="1" colspan="1">switch</td><td valign="top" align="left" rowspan="1" colspan="1">EDS errors</td><td valign="top" align="char" char="." rowspan="1" colspan="1">−0.36</td><td valign="top" align="char" char="." rowspan="1" colspan="1">1.23</td></tr></tbody></table><table-wrap-foot><p><italic>SOC, Stockings of Cambridge; SWM, Spatial Working Memory; SSP, Spatial Span; IED, Intra-dimensional/Extra-dimensional Shifts. The right two columns indicate the mean value and standard deviation (SD) of the scores from 90 participants, respectively</italic>.</p></table-wrap-foot></table-wrap><p>The SWM assessed the ability to retain spatial information and to manipulate remembered items in working memory and heuristic strategy. The test began with a specific number of colored squares (boxes) shown on the screen. Participants were required to find one blue “token” in each of the square through the boxes with an increasing number (3 to 8). When a blue token was found, it would be used to fill up an empty column on the right hand side of the screen. The reveal time for the empty content of the box was 1000 ms. The outcome scores of “between errors” and strategy were chosen to measure working memory (Table <xref ref-type="table" rid="T2">2</xref>).</p><p>The SSP measured the capacity of spatial short-term memory. In this task, nine white boxes were displayed on the screen at the beginning and then some of them would change color in a specific order. Participants were asked to repeat the order by clicking the boxes which had changed color. The difficulty level ranged from 2 to 9 boxes. The stimulus duration of color changing was 3000 ms, and the inter-stimulus time was 500 ms. After the last box in the sequence had reverted to white, there would be a delay of 1000 ms followed by a beep with a duration of 1000 ms (interim sound duration). The outcome scores of span length and total errors were chosen to measure short-term memory (Table <xref ref-type="table" rid="T2">2</xref>).</p><p>The IED assessed the ability of attention set shifting which included inhibition and switch. The participants needed to determine the rules by clicking on one of the two colored graphics randomly in each trial and make the right choice according to the feedback of computer. The rules would change in a new stage (after six trials of consecutive right choices). If the participant still did not meet the criterion above after 50 trials at any stage, the test was terminated. These transformations included a reinforced stimulus dimension (intra-dimensional shift) corresponding to the ability of inhibition, and a previously irrelevant stimulus dimension (extra-dimensional shift) corresponding to the ability of switch. There was a pause of 250 ms (pre-stimulus pause) before the test stimuli were added to the boxes. The feedback of choice was displayed for 1500 ms in each trial, and following the feedback there was a blank screen with a duration of 1000 ms. The outcome scores of errors made prior to the extra-dimensional shift of the task (Pre-ED errors) were chosen to measure inhibition, and the outcome scores of errors made in the extra-dimensional shift stage (EDS errors) were chosen to measure switch (Table <xref ref-type="table" rid="T2">2</xref>).</p><p>The correlation between each of component of EF score was listed in Table <xref ref-type="table" rid="T3">3</xref>.</p><table-wrap id="T3" position="float"><label>Table 3</label><caption><p><bold>The correlation between each of component of EF score</bold>.</p></caption><table frame="hsides" rules="groups"><thead><tr><th valign="top" align="left" rowspan="1" colspan="1"><bold>Components of EF</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>planning</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>working memory</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>short-term memory</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>inhibition</bold></th><th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>switch</bold></th></tr><tr><th rowspan="1" colspan="1"></th><th valign="top" align="center" rowspan="1" colspan="1"><bold><italic>R</italic></bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold><italic>p</italic></bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold><italic>R</italic></bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold><italic>p</italic></bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold><italic>R</italic></bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold><italic>p</italic></bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold><italic>R</italic></bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold><italic>p</italic></bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold><italic>R</italic></bold></th><th valign="top" align="center" rowspan="1" colspan="1"><bold><italic>p</italic></bold></th></tr></thead><tbody><tr><td valign="top" align="left" rowspan="1" colspan="1">planning</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" rowspan="1" colspan="1"><bold>0.234</bold></td><td valign="top" align="center" rowspan="1" colspan="1"><bold>0.026</bold></td><td valign="top" align="center" rowspan="1" colspan="1">0.110</td><td valign="top" align="center" rowspan="1" colspan="1">0.303</td><td valign="top" align="center" rowspan="1" colspan="1">−0.056</td><td valign="top" align="center" rowspan="1" colspan="1">0.602</td><td valign="top" align="center" rowspan="1" colspan="1">0.072</td><td valign="top" align="center" rowspan="1" colspan="1">0.499</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">working memory</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" rowspan="1" colspan="1">0.016</td><td valign="top" align="center" rowspan="1" colspan="1">0.882</td><td valign="top" align="center" rowspan="1" colspan="1">0.022</td><td valign="top" align="center" rowspan="1" colspan="1">0.836</td><td valign="top" align="center" rowspan="1" colspan="1">−0.034</td><td valign="top" align="center" rowspan="1" colspan="1">0.748</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">short-term memory</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" rowspan="1" colspan="1">0.080</td><td valign="top" align="center" rowspan="1" colspan="1">0.454</td><td valign="top" align="center" rowspan="1" colspan="1">0.032</td><td valign="top" align="center" rowspan="1" colspan="1">0.762</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">inhibition</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" rowspan="1" colspan="1">−0.025</td><td valign="top" align="center" rowspan="1" colspan="1">0.815</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">switch</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" colspan="2" rowspan="1">—</td><td valign="top" align="center" colspan="2" rowspan="1">—</td></tr></tbody></table><table-wrap-foot><p><italic>The bold values represent the correlation coefficient and the corresponding p-value with significant correlation</italic>.</p></table-wrap-foot></table-wrap></sec><sec><title>Construction of the brain functional networks</title><p>The complex brain network analysis was applied to our resting-state data. The most critical elements of a complex network are nodes and edges. The nodes were defined as the positions of the 24 NIRS channels and the edges were defined as functional connectivity between the node pairs. By calculating the Pearson correlation coefficients of time courses between each pair of nodes to quantify the functional connectivity, a 24 × 24 correlation matrix was obtained for each participant. Then each correlation matrix was converted to binary matrixes by applying fixed thresholds (network density). In this study, the thresholds were set over a wide network density range (10–46%) at the intervals of 1% because the wide density range could maintain the small-world properties (Watts and Strogatz, <xref rid="B67" ref-type="bibr">1998</xref>; Tian et al., <xref rid="B60" ref-type="bibr">2011</xref>).</p></sec><sec><title>Network analysis</title><sec><title>Evaluation of the small-world property</title><p>To evaluate whether our resting-state data indeed had the small-world properties, we first calculated our small-world network parameters to see whether they met the following criteria:
<disp-formula id="E1"><label>(1)</label><mml:math id="M1"><mml:mrow><mml:mo>γ</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo>></mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>λ</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo>≈</mml:mo><mml:mn>1</mml:mn><mml:mtext> </mml:mtext><mml:mi>a</mml:mi><mml:mi>n</mml:mi><mml:mi>d</mml:mi><mml:mtext> </mml:mtext><mml:mo>σ</mml:mo><mml:mo>=</mml:mo><mml:mo>γ</mml:mo><mml:mo>/</mml:mo><mml:mo>λ</mml:mo><mml:mo>></mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math></disp-formula>where <italic>C</italic><sub><italic>net</italic></sub> and <italic>L</italic><sub><italic>net</italic></sub> represent the clustering coefficient and the characteristic path length [see basic definitions (4) and (5)] of our real networks, respectively, and <italic>C</italic><sub><italic>ran</italic></sub> and <italic>L</italic><sub><italic>ran</italic></sub> are the corresponding indices drawn from the average of 100 matched random networks (Watts and Strogatz, <xref rid="B67" ref-type="bibr">1998</xref>; Maslov and Sneppen, <xref rid="B36" ref-type="bibr">2002</xref>; Tian et al., <xref rid="B60" ref-type="bibr">2011</xref>).</p><p>The small-world property of network could be summarized as: σ = γ/λ > 1. In this study, we carried out the statistical tests to further verify σ > 1. Specifically, for each of network threshold range (10–46%, at the intervals of 1%), we tested whether σ significantly was greater than 1 across all the participants using a one-sample <italic>t</italic>-test.</p></sec><sec><title>Network topological properties</title><p>We obtained seven network topological properties metrics including three regional nodal parameters (nodal degree <italic>D</italic><sub><italic>nod</italic></sub>, nodal efficiency <italic>E</italic><sub><italic>nod</italic></sub>, nodal betweenness centrality <italic>N</italic><sub><italic>bc</italic></sub>), and four global parameters (clustering coefficient <italic>C</italic><sub><italic>p</italic></sub>, characteristic path length <italic>L</italic><sub><italic>p</italic></sub>, global efficiency <italic>E</italic><sub><italic>glob</italic></sub>, local efficiency <italic>E</italic><sub><italic>loc</italic></sub>) by using the graph theoretical network analysis toolbox: GRETNA (<ext-link ext-link-type="uri" xlink:href="http://www.nitrc.org/projects/gretna/">http://www.nitrc.org/projects/gretna/</ext-link>). Although there are many parameters to characterize network functional connectivity, we chose these particular parameters because they have been commonly used in the existing neuroimaging studies to characterize the global and regional nodal network properties (Li et al., <xref rid="B32" ref-type="bibr">2009</xref>; Tian et al., <xref rid="B60" ref-type="bibr">2011</xref>; Niu et al., <xref rid="B43" ref-type="bibr">2012</xref>). Second and more specifically, some previous studies (Tian et al., <xref rid="B60" ref-type="bibr">2011</xref>; Niu et al., <xref rid="B43" ref-type="bibr">2012</xref>; Wang et al., <xref rid="B66" ref-type="bibr">2012</xref>) have used these network parameters to characterize resting-state network functional connectivities. To ensure comparability between these existing network functional connectivity studies and the present study, we chose to use these specific parameters.</p><p>For a graph <italic>G, N</italic> represented the total number of nodes in the network. The basic definitions are listed as follows.</p><p>Nodal degree [<italic>D</italic><sub><italic>nod</italic></sub>(<italic>i</italic>)] is the number of edges linked to the node <italic>i</italic>. <italic>D</italic><sub><italic>nod</italic></sub>(<italic>i</italic>) reflectes the importance of node <italic>i</italic> in the network structure (Tian et al., <xref rid="B60" ref-type="bibr">2011</xref>; Wang et al., <xref rid="B66" ref-type="bibr">2012</xref>).</p><p>Nodal efficiency [<italic>E</italic><sub><italic>nod</italic></sub>(<italic>i</italic>)] (Achard and Bullmore, <xref rid="B1" ref-type="bibr">2007</xref>):
<disp-formula id="E2"><label>(2)</label><mml:math id="M2"><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mfrac><mml:mstyle displaystyle="true"><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mtext> </mml:mtext><mml:mo>≠</mml:mo><mml:mtext> </mml:mtext><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:mi>G</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mstyle></mml:mrow></mml:math></disp-formula>where <italic>L</italic><sub><italic>ij</italic></sub> represents the shortest path length from node <italic>i</italic> to <italic>j</italic>. The <italic>E</italic><sub><italic>nod</italic></sub>(<italic>i</italic>) measures the information transfer efficiency between the node <italic>i</italic> and other nodes (Wang et al., <xref rid="B66" ref-type="bibr">2012</xref>).</p><p>Nodal betweenness centrality [<italic>N</italic><sub><italic>bc</italic></sub>(<italic>i</italic>)] (Freeman, <xref rid="B20" ref-type="bibr">1977</xref>):
<disp-formula id="E3"><label>(3)</label><mml:math id="M3"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mrow><mml:mi>b</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mtext>=</mml:mtext><mml:mstyle displaystyle="true"><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mtext> </mml:mtext><mml:mo>≠</mml:mo><mml:mtext> </mml:mtext><mml:mi>i</mml:mi><mml:mtext> </mml:mtext><mml:mo>≠</mml:mo><mml:mtext> </mml:mtext><mml:mi>k</mml:mi><mml:mo>∈</mml:mo><mml:mi>G</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mo>δ</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mrow><mml:msub><mml:mo>δ</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mstyle></mml:mrow></mml:math></disp-formula>where δ<sub><italic>jk</italic></sub> represents the number of shortest paths from node <italic>j</italic> to node <italic>k</italic>, and δ<sub><italic>jk</italic></sub>(<italic>i</italic>) represents the number of shortest paths from node <italic>j</italic> to node <italic>k</italic> passing through node <italic>i</italic> within graph <italic>G</italic>. The <italic>N</italic><sub><italic>bc</italic></sub>(<italic>i</italic>) reflects the importance of node <italic>i</italic> over information flow in the entire network (Tian et al., <xref rid="B60" ref-type="bibr">2011</xref>; Xu et al., <xref rid="B71" ref-type="bibr">2014</xref>).</p><p>Nodal degree measures the local interconnection capability of a brain region (Wang et al., <xref rid="B66" ref-type="bibr">2012</xref>), nodal efficiency the transfer capability (Wang et al., <xref rid="B66" ref-type="bibr">2012</xref>), and nodal betweenness centrality the frequency of participation in information transition of specific areas over the whole network (Freeman, <xref rid="B20" ref-type="bibr">1977</xref>; Achard and Bullmore, <xref rid="B1" ref-type="bibr">2007</xref>; Wang et al., <xref rid="B66" ref-type="bibr">2012</xref>; Gao et al., <xref rid="B24" ref-type="bibr">2013</xref>). These three regional parameters reflect the importance of the specialization and integration of information processing of specific brain areas in the whole functional network (Freeman, <xref rid="B20" ref-type="bibr">1977</xref>; Achard and Bullmore, <xref rid="B1" ref-type="bibr">2007</xref>; Li et al., <xref rid="B32" ref-type="bibr">2009</xref>; Tian et al., <xref rid="B60" ref-type="bibr">2011</xref>; Xu et al., <xref rid="B71" ref-type="bibr">2014</xref>).</p><p>Clustering coefficient <italic>C</italic><sub><italic>P</italic></sub> (Watts and Strogatz, <xref rid="B67" ref-type="bibr">1998</xref>):
<disp-formula id="E4"><label>(4)</label><mml:math id="M4"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>P</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>N</mml:mi></mml:mfrac><mml:mstyle displaystyle="true"><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:mi>G</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:msub><mml:mi>E</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo>−</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mfrac></mml:mrow></mml:mstyle></mml:mrow></mml:math></disp-formula>where <italic>E</italic><sub><italic>i</italic></sub> is the number of edges in the subgraph <italic>G</italic><sub><italic>i</italic></sub> which consists of the neighbors of node <italic>i</italic>.</p><p>Characteristic path length <italic>L</italic><sub><italic>P</italic></sub> (Newman, <xref rid="B40" ref-type="bibr">2003</xref>):
<disp-formula id="E5"><label>(5)</label><mml:math id="M5"><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mi>P</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>N</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mfrac><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mstyle displaystyle="true"><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mtext> </mml:mtext><mml:mo>≠</mml:mo><mml:mtext> </mml:mtext><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:mi>G</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mstyle></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mfrac></mml:mrow></mml:math></disp-formula></p><p>Global efficiency <italic>E</italic><sub><italic>glob</italic></sub> (Latora and Marchiori, <xref rid="B30" ref-type="bibr">2001</xref>):
<disp-formula id="E6"><label>(6)</label><mml:math id="M6"><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi>g</mml:mi><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>b</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>N</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>N</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mfrac><mml:mstyle displaystyle="true"><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mtext> </mml:mtext><mml:mo>≠</mml:mo><mml:mtext> </mml:mtext><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:mi>G</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mstyle></mml:mrow></mml:math></disp-formula></p><p>Local efficiency <italic>E</italic><sub><italic>loc</italic></sub> (Latora and Marchiori, <xref rid="B30" ref-type="bibr">2001</xref>):
<disp-formula id="E7"><label>(7)</label><mml:math id="M7"><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>N</mml:mi></mml:mfrac><mml:mstyle displaystyle="true"><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:mi>G</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi>g</mml:mi><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>b</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mstyle></mml:mrow></mml:math></disp-formula>where <italic>E</italic><sub><italic>glob</italic></sub>(<italic>i</italic>) is the global efficiency of <italic>G</italic><sub><italic>i</italic></sub>.</p><p><italic>C</italic><sub><italic>P</italic></sub> and <italic>E</italic><sub><italic>loc</italic></sub> measure the clustering degree of the entire network, whereas <italic>L</italic><sub><italic>P</italic></sub> and <italic>E</italic><sub><italic>glob</italic></sub> measure the global information transfer efficiency of a network with the former being the inverse of the latter (Tian et al., <xref rid="B60" ref-type="bibr">2011</xref>).</p><p>We calculated the integral quantity to obtain the summations of each network density for every participant (Tian et al., <xref rid="B60" ref-type="bibr">2011</xref>). For the global network parameters,
<disp-formula id="E8"><label>(8)</label><mml:math id="M8"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mrow><mml:mi>g</mml:mi><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>b</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mtext> </mml:mtext><mml:mo>=</mml:mo><mml:mtext> </mml:mtext><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mn>46</mml:mn></mml:mrow></mml:msubsup><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>k</mml:mi><mml:mi>Δ</mml:mi><mml:mi>s</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mstyle><mml:mi>Δ</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:math></disp-formula>where Δ<italic>s</italic> is the density interval of 1%; <italic>P</italic>(<italic>kΔs</italic>) represents the global network parameter at the network density of <italic>kΔs</italic>.</p><p>For the regional nodal parameters:
<disp-formula id="E9"><label>(9)</label><mml:math id="M9"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mtext> </mml:mtext><mml:mo>=</mml:mo><mml:mtext> </mml:mtext><mml:mo>10</mml:mo></mml:mrow><mml:mrow><mml:mn>46</mml:mn></mml:mrow></mml:msubsup><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mi>Δ</mml:mi><mml:mi>s</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mstyle><mml:mi>Δ</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:math></disp-formula>where <italic>P</italic>(<italic>i, kΔs</italic>) represents a regional nodal parameter of the node <italic>i</italic> at the network density of <italic>kΔs</italic>.</p></sec></sec><sec><title>Correlation analyses</title><p>We used scores produced by each of the four tasks in CANTAB as the raw scores. All raw scores were then converted to z-scores before further analyses:
<disp-formula id="E10"><label>(10)</label><mml:math id="M10"><mml:mrow><mml:mi>Z</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>X</mml:mi><mml:mo>−</mml:mo><mml:mi>M</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mi>S</mml:mi></mml:mfrac></mml:mrow></mml:math></disp-formula>where <italic>X</italic> is the raw scores, <italic>M</italic> is the mean value of the raw scores from the entire sample, and <italic>S</italic> is the standard deviation of the raw scores from the entire sample.</p><p>Because the working memory task produced two raw scores (i.e., between errors and strategy; Table <xref ref-type="table" rid="T2">2</xref>), we first normalized each of these two raw scores, and then added up these two normalized scores as an index of their performance of working memory. We did the same for the short-term memory task, namely that we added up the normalized scores of span length and total errors as an index of their performance of the short-term memory task. With respect to the two raw scores of IED, they actually indicated two performances (i.e., inhibition and switch), respectively. We therefore only normalized each of these two raw scores. Thus, in total, we obtained five scores: planning, working memory, short-term memory, inhibition, and switch (Table <xref ref-type="table" rid="T2">2</xref>). Additionally, we added up all of these five normalized scores to obtain the Total EF score.</p><p>Then, to examine the linkage between resting-state functional neutral network topological properties and EF performances, we performed correlation analyses of neural network topological property and behavioral EF scores with the permutations tests. We first computed correlation coefficient of each EF score with each network topological index. Then, we performed a permutation test to assess the statistical significance of the coefficient.</p><p>Below we used Total EF score and nodal degree to illustrate how such test was implemented.</p><list list-type="order"><list-item><p>We obtained a Pearson correlation coefficient between Total EF score and nodal degree with the data from all 90 participants to obtain a non-permuted correlation coefficients.</p></list-item><list-item><p>Total EF scores were scrambled (randomly permuted) and were correlated with nodal degree (unscrambled) to obtain a permuted Pearson correlation coefficient. We did so 1000 times.</p></list-item><list-item><p>Based on (2), we ordered the permuted correlation coefficients according to their values from the lowest to the highest. We obtained the 25th values from both the top and bottom of the distribution to establish 95% bilateral confidence intervals. The <italic>p</italic> values are defined as (Tian et al., <xref rid="B59" ref-type="bibr">2016</xref>):
<disp-formula id="E11"><label>(11)</label><mml:math id="M11"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mtext>1 </mml:mtext><mml:mo>+</mml:mo><mml:mtext> </mml:mtext><mml:msub><mml:mi>N</mml:mi><mml:mrow><mml:mi>g</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mtext>1 </mml:mtext><mml:mo>+</mml:mo><mml:mtext> </mml:mtext><mml:mi>N</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></disp-formula></p><p>Where <italic>N</italic> is the number of permutations, and here <italic>N</italic> = 1000; <italic>N</italic><sub><italic>greater</italic></sub> is the number of permuted correlation coefficients whose absolute value is greater than that of absolute value of non-permuted correlation coefficients. If <italic>p</italic> < 0.05, the non-permuted correlation coefficient is considered significant.</p></list-item></list></sec></sec><sec id="s3"><title>Result</title><sec><title>Small-world properties of network</title><p>We found that over the network density range of 10–46%, γ was greater than 1, λ was approximately equal to 1, σ was greater than 1 and decreased gradually with the increase of density (Figure <xref ref-type="fig" rid="F2">2A</xref>). One-sample <italic>t</italic>-test showed that the t-statistic values of σ was far greater than the t-statistic critical value at the significant level of <italic>p</italic> < 0.05 (one-tailed) (Figure <xref ref-type="fig" rid="F3">3</xref>). The result verified σ>1 of the networks in our study. Thus, the functional network in the PFC showed prominent small-world properties similar to those with the whole brain functional network reported in the previous studies (Tian et al., <xref rid="B60" ref-type="bibr">2011</xref>; Niu et al., <xref rid="B43" ref-type="bibr">2012</xref>). Additional results (<italic>C</italic><sub><italic>P</italic></sub>, <italic>L</italic><sub><italic>P</italic></sub>, <italic>E</italic><sub><italic>glob</italic></sub>, <italic>E</italic><sub><italic>loc</italic></sub>) (Figures <xref ref-type="fig" rid="F2">2B,C</xref>) further supported the small-world properties, which integrated the high information processing efficiency and local connectivity effectively (Watts and Strogatz, <xref rid="B67" ref-type="bibr">1998</xref>; Li et al., <xref rid="B32" ref-type="bibr">2009</xref>; Tian et al., <xref rid="B60" ref-type="bibr">2011</xref>; Niu et al., <xref rid="B43" ref-type="bibr">2012</xref>).</p><fig id="F2" position="float"><label>Figure 2</label><caption><p><bold>The small-world properties and efficiency of the resting-state functional network</bold>. <bold>(A)</bold> Normalized parameters (γ and λ) and small-world parameters (σ). <bold>(B)</bold> Clustering coefficient (<italic>C</italic><sub><italic>p</italic></sub>) and characteristic path length (<italic>L</italic><sub><italic>p</italic></sub>). <bold>(C)</bold> Global efficiency (<italic>E</italic><sub><italic>glob</italic></sub>) and local efficiency (<italic>E</italic><sub><italic>loc</italic></sub>). The standard errors were shown on the figures. The parametric statistical values were averaged over the networks of all individuals (removing abnormal data based on the triple standard difference method).</p></caption><graphic xlink:href="fnins-10-00452-g0002"></graphic></fig><fig id="F3" position="float"><label>Figure 3</label><caption><p><bold>One-sample <italic>t</italic>-test statistics of σ (compared to 1) at each network threshold</bold>. The vertical axis is the t-statistic values of the one-sample <italic>t</italic>-test. The blue line represents the t-statistic values of σ at each network threshold, and the red line represents the t-statistic critical value (<italic>T</italic> = 1.662) at the significant level of <italic>p</italic> = 0.05 (one-tailed).</p></caption><graphic xlink:href="fnins-10-00452-g0003"></graphic></fig></sec><sec><title>Relations between network properties and behavioral EF scores</title><p>The behavioral results of EF performances included scores of Total EF, planning, working memory, short-term memory, inhibition, and switch provided by CANTAB (Table <xref ref-type="table" rid="T2">2</xref>).</p><p>None of the behavioral EF scores were correlated with any of the global network topological parameters. However, the behavioral EF scores were significantly correlated with several nodal network topological parameters.</p><p>The scores of Total EF were significantly and positively correlated with nodal efficiency (<italic>E</italic><sub><italic>nod</italic></sub>) in the right SFG (<italic>p</italic> = 0.036, channel 15, BA 10; Figure <xref ref-type="fig" rid="F4">4A</xref>); The scores of Total EF were also significantly and negatively correlated with nodal efficiency (<italic>E</italic><sub><italic>nod</italic></sub>) the right IFG (<italic>p</italic> = 0.044, channel 17, BA 45; Figure <xref ref-type="fig" rid="F4">4A</xref>), and nodal betweenness centrality (<italic>N</italic><sub><italic>bc</italic></sub>) in the right IFG (<italic>p</italic> = 0.043, channel 17, BA 45; Figure <xref ref-type="fig" rid="F4">4B</xref>).</p><fig id="F4" position="float"><label>Figure 4</label><caption><p><bold>The estimated positions of the cortical regions showing significant correlation between the scores of Total EF and the regional nodal topological properties of the network</bold>. <bold>(A)</bold> Total EF and nodal efficiency. <bold>(B)</bold> Total EF and nodal betweenness centrality. These positions are labeled using channel number in red circles (positive correlation) or green circles (negative correlation) with respective MNI coordinates shown below. The left and right subgraphs show the permutations tests results of correlation coefficients. The shaded parts are the reject regions of permutations tests at significant level 5% (two-tailed) and the <italic>p</italic>-<italic>values</italic> are shown in the subgraphs. The red line and green lines indicate the positive and negative non-permuted correlation coefficients, respectively. Abbreviation: SFG (dor), dorsal superior frontal gyrus; IFG (triang), triangular inferior frontal gyrus.</p></caption><graphic xlink:href="fnins-10-00452-g0004"></graphic></fig><p>The scores of planning were significantly and positively correlated with nodal degree (<italic>D</italic><sub><italic>nod</italic></sub>, <italic>p</italic> = 0.021; Figure <xref ref-type="fig" rid="F5">5A</xref>), nodal efficiency (<italic>E</italic><sub><italic>nod</italic></sub>, <italic>p</italic> = 0.023; Figure <xref ref-type="fig" rid="F5">5B</xref>) and nodal betweenness centrality (<italic>N</italic><sub><italic>bc</italic></sub>, <italic>p</italic> = 0.019; Figure <xref ref-type="fig" rid="F5">5C</xref>) in the right MFG (channel 21, BA 46). The scores of working memory were significantly and negatively correlated with nodal degree (<italic>D</italic><sub><italic>nod</italic></sub>) in the right MFG (<italic>p</italic> = 0.028, channel 24, BA 9; Figure <xref ref-type="fig" rid="F6">6A</xref>), nodal efficiency (<italic>E</italic><sub><italic>nod</italic></sub>) in the left orbital MFG (<italic>p</italic> = 0.008, channel 1, BA 46), the right IFG (<italic>p</italic> = 0.029, channel 14, BA 46), the right IFG (<italic>p</italic> < 0.001, channel 17, BA 45) and the right MFG (<italic>p</italic> = 0.042, channel 19, BA 46; Figure <xref ref-type="fig" rid="F6">6B</xref>), and nodal betweenness centrality (<italic>N</italic><sub><italic>bc</italic></sub>) in the right IFG (<italic>p</italic> = 0.003, channel 17, BA 45; Figure <xref ref-type="fig" rid="F6">6C</xref>). The scores of working memory were also significantly and positively correlated with the right MFG (<italic>p</italic> = 0.039, channel 21, BA 46; Figure <xref ref-type="fig" rid="F6">6C</xref>). The scores of short-term memory were significantly and positively correlated with nodal degree (<italic>D</italic><sub><italic>nod</italic></sub>, <italic>p</italic> = 0.008; (Figure <xref ref-type="fig" rid="F7">7A</xref>), nodal efficiency (<italic>E</italic><sub><italic>nod</italic></sub>, <italic>p</italic> = 0.004; Figure <xref ref-type="fig" rid="F7">7B</xref>) and nodal betweenness centrality (<italic>N</italic><sub><italic>bc</italic></sub>, <italic>p</italic> = 0.025; Figure <xref ref-type="fig" rid="F7">7C</xref>) in the left SFG (channel 5, BA 10).</p><fig id="F5" position="float"><label>Figure 5</label><caption><p><bold>The estimated positions of the cortical regions showing significant correlation between the scores of planning and the regional nodal topological properties of the network</bold>. <bold>(A)</bold> Planning and nodal degree. <bold>(B)</bold> Planning and nodal efficiency. <bold>(C)</bold> Planning and nodal betweenness centrality. These positions are labeled using channel number in red circles (positive correlation) with respective MNI coordinates shown below. The left subgraphs show the permutations tests results of correlation coefficients. The shaded parts are the reject regions of permutations tests at significant level 5% (two-tailed) and the <italic>p</italic>-<italic>values</italic> are shown in the subgraphs. The red line indicates the positive non-permuted correlation coefficients. Abbreviation: MFG, middle frontal gyrus.</p></caption><graphic xlink:href="fnins-10-00452-g0005"></graphic></fig><fig id="F6" position="float"><label>Figure 6</label><caption><p><bold>The estimated positions of the cortical regions showing significant correlation between the scores of working memory and the regional nodal topological properties of the network</bold>. <bold>(A)</bold> Working memory and nodal degree. <bold>(B)</bold> Working memory and nodal efficiency. <bold>(C)</bold> Working memory and nodal betweenness centrality. These positions are labeled using channel number in red circles (positive correlation) or green circles (negative correlation) with respective MNI coordinates shown below. The left and right subgraphs show the permutations tests results of correlation coefficients. The shaded parts are the reject regions of permutations tests at significant level 5% (two-tailed) and the <italic>p</italic>-<italic>values</italic> are shown in the subgraphs. The red line and green lines indicate the positive and negative non-permuted correlation coefficients, respectively. Abbreviation: MFG, middle frontal gyrus; ORBmed, Orbitofrontal cortex (medial); IFG (triang), triangular inferior frontal gyrus.</p></caption><graphic xlink:href="fnins-10-00452-g0006"></graphic></fig><fig id="F7" position="float"><label>Figure 7</label><caption><p><bold>The estimated positions of the cortical regions showing significant correlation between the scores of short-term memory and the regional nodal topological properties of the network</bold>. <bold>(A)</bold> Short-term memory and nodal degree. <bold>(B)</bold> Short-term memory and nodal efficiency. <bold>(C)</bold> Short-term memory and nodal betweenness centrality. These positions are labeled using channel number in red circles (positive correlation) with respective MNI coordinates shown below. The left subgraphs show the permutations tests results of correlation coefficients. The shaded parts are the reject regions of permutations tests at significant level 5% (two-tailed) and the <italic>p</italic>-<italic>values</italic> are shown in the subgraphs. The red line indicates the positive non-permuted correlation coefficients. Abbreviation: SFG (dor), dorsal superior frontal gyrus.</p></caption><graphic xlink:href="fnins-10-00452-g0007"></graphic></fig><p>The scores of inhibition were not significantly correlated with any of the regional nodal network topological parameters. The scores of switch were significantly and positively correlated with nodal degree (<italic>D</italic><sub><italic>nod</italic></sub>, <italic>p</italic> = 0.007; Figure <xref ref-type="fig" rid="F8">8A</xref>) and nodal betweenness centrality (<italic>N</italic><sub><italic>bc</italic></sub>, <italic>p</italic> = 0.019; Figure <xref ref-type="fig" rid="F8">8B</xref>) in the right MFG (channel 19, BA 46).</p><fig id="F8" position="float"><label>Figure 8</label><caption><p><bold>The estimated positions of the cortical regions showing significant correlation between the scores of switch and the regional nodal topological properties of the network</bold>. <bold>(A)</bold> Switch and nodal degree. <bold>(B)</bold> Switch and nodal betweenness centrality. These positions are labeled using channel number in red circles (positive correlation) with respective MNI coordinates shown below. The left subgraphs show the permutations tests results of correlation coefficients. The shaded parts are the reject regions of permutations tests at significant level 5% (two-tailed) and the <italic>p</italic>-<italic>values</italic> are shown in the subgraphs. The red line indicates the positive non-permuted correlation coefficients. Abbreviation: MFG, middle frontal gyrus.</p></caption><graphic xlink:href="fnins-10-00452-g0008"></graphic></fig></sec></sec><sec sec-type="discussion" id="s4"><title>Discussion</title><p>In this study, we investigated the relations between behavioral performance in various EF tasks and the fNIRS resting-state functional network global and local topological properties in the PFC. We obtained several major findings.</p><sec><title>Small-world network properties</title><p>We found prominent small-world properties in our participants' resting-state brain functional networks. This finding is consistent with the results of several fMRI resting-state studies (Achard et al., <xref rid="B2" ref-type="bibr">2006</xref>; Tian et al., <xref rid="B60" ref-type="bibr">2011</xref>). More specifically, our findings are in line with an existing fNIRS resting-state study (Niu et al., <xref rid="B43" ref-type="bibr">2012</xref>) reporting that the whole brain resting-state network has prominent small-world network properties. The present finding taken together with those from Niu et al. (<xref rid="B43" ref-type="bibr">2012</xref>) suggested that small-world properties are the essential characteristics of brain resting-state network. Further, our finding suggested that fNIRS is an effective technology to describe the topological properties of the brain functional network.</p></sec><sec><title>Relations between behavioral EF scores and neural network parameters</title><p>We found that none of the four global parameters (i.e., clustering coefficient, characteristic path length, global efficiency and local efficiency) was related to any of the behavioral EF scores. However, EF scores were significantly correlated with the regional nodal network parameters (i.e., nodal degree, nodal efficiency and nodal betweenness centrality). These findings support the Local Property Only Hypothesis.</p><p>Our findings are thus in contrast to existing studies that examined the relations between intelligence and the network properties of the resting-state functional connectivity in healthy adults (van den Heuvel et al., <xref rid="B62" ref-type="bibr">2009</xref>; Langer et al., <xref rid="B29" ref-type="bibr">2012</xref>). They found that intelligence was significantly correlated with the global network indexes. Our findings taken together with these existing studies suggested that the global network properties may underlie such a general ability as intelligence, whereas the network regional properties may subserve such a special cognitive ability as EF. In other words, the specific aspects of the network properties of the resting-state brain network play different roles for different cognitive abilities. As EF involves a specialized set of cortical regions and the connections among them, the regional quality of the resting-state network matters more than its global quality.</p><p>This interpretation is consistent with our findings that Total EF scores and different components of EF are associated with the regional network properties in different cortical areas. For the Total EF scores, the better nodal efficiency the higher the Total EF scores in the right dorsal SFG, whereas the opposite pattern was observed in the right triangular IFG (the better nodal efficiency and nodal betweenness centrality the lower Total EF scores; Figure <xref ref-type="fig" rid="F4">4</xref>). The opposite patterns of the Total EF scores might reflect the different roles of SFG and IFG in EF (Petrides, <xref rid="B49" ref-type="bibr">2005</xref>). A recent fMRI study (Reineberg et al., <xref rid="B51" ref-type="bibr">2015</xref>) examined the relations between Total EF scores and resting-state functional connectivity. They found that the Total EF was associated with functional connectivity intensity of the right frontoparietal resting-state functional network, which was consistent with our findings. We found that better information transfer of SFG was associated with higher Total EF scores, and better information transfer of IFG was linked to lower Total EF scores. Although the specific functions of SFG and IFG for EF are yet to be ascertained, our findings support the idea that the right PFC in the resting-state is crucial to EF overall.</p><p>For planning, we found that the ability of planning was positively correlated with all the three resting-state regional network parameters in the right MFG (BA46) (Figure <xref ref-type="fig" rid="F5">5</xref>). A study by Woo et al. (<xref rid="B69" ref-type="bibr">2010</xref>) using positron emission tomography (PET) showed that poor planning performances in patients with Alzheimer's disease were associated with lower resting metabolism in the right MFG and adjacent IFG (BA45 and BA46). Woo et al. (<xref rid="B69" ref-type="bibr">2010</xref>) suggested that the right PFC was necessary to foster planning ability that was consistent with our findings. Moreover, a previous task-based fMRI study (Newman et al., <xref rid="B41" ref-type="bibr">2003</xref>) showed that although both the right and left PFC were activated by a planning task, the activation in the right dorsolateral prefrontal cortex (DLPFC) was attenuated by task difficulty. Newman et al. (<xref rid="B41" ref-type="bibr">2003</xref>) confirmed that the right PFC areas were involved in information generation and integration of planning and strategy. In addition, Petrides, (2005) suggested the middle DLPFC (BA 46, and 9/46) could control cognition and planned behavior consciously. Our findings further sugested that the right MFG may be a central area for the planning ability with both high local association capability and high information transfer level. In other words, the resting-state network quality of the right MFG may play a key role in performing organizational planning functions during cognitive processes (Woo et al., <xref rid="B69" ref-type="bibr">2010</xref>).</p><p>For working memory, the score of spatial working memory was significantly correlated with nodal degree in the right MFG (BA 9) (Figure <xref ref-type="fig" rid="F6">6A</xref>). The score of working memory was also significantly correlated with the nodal efficiency and nodal betweenness centrality in parts of the right triangular IFG and the adjacent MFG (BA 45 and 46) (Figures <xref ref-type="fig" rid="F6">6B,C</xref>). In addition, working memory was significantly correlated with nodal efficiency of the left orbitofrontal cortex (BA46) (Figure <xref ref-type="fig" rid="F6">6B</xref>). Our findings are generally consistent with the findings of a recent study by Zou et al. (<xref rid="B74" ref-type="bibr">2013</xref>). They found that the amplitude of low-frequency fluctuation (ALFF) in the MFG was related to the task-evoked activation associated with working memory. Given the fact that the scores of planning and those of working memory were significantly correlated (<italic>r</italic> = 0.23; <italic>p</italic> = 0.026; Table <xref ref-type="table" rid="T3">3</xref>), our findings suggested that the MFG may be involved in information processing underlying both planning and working memory. Additionally, based on the negative correlations in our results in the right triangular IFG and left orbitofrontal cortex, the high internally spontaneous activity levels, especially information transfer levels of these areas of PFC, may be detrimental to one's working memory.</p><p>The significant positive correlations between the score of short-term memory and all the three resting-state regional network characteristics were localized in the left SFG (BA10) (Figure <xref ref-type="fig" rid="F7">7</xref>). An fMRI study by Wu et al. (<xref rid="B70" ref-type="bibr">2014</xref>) showed that patients with amnestic mild cognitive impairment indicated both significantly declined short-term memory and reduced resting-state connectivity strength in the bilateral DLPFC. In particular, among the findings of Wu et al. (<xref rid="B70" ref-type="bibr">2014</xref>), the functional connectivity of the left SFG (BA 10) was significantly reduced, consistent with our results. Our findings support the relationship between the spontaneous activity in the left frontal pole and the ability of short-term memory. In addition, combining with the results of working memory, it suggested that the different component of memory function involved different areas of PFC in resting-state.</p><p>The EF scores for inhibition showed no significant correlation with any of the regional nodal network topological parameters. In contrast, the EF scores for switch were significantly positively correlated with the nodal degree and nodal betweenness centrality in the right MFG (BA 46) (Figure <xref ref-type="fig" rid="F8">8</xref>). This finding suggested that the increased capabilities of both local association and frequency of information transition of the right MFG (BA 46) are linked to the increased switch level. Our results are not entirely in line with the existing studies that examined the activations of cortical regions involved in performing actual inhibition and switch tasks. For example, the existing studies examining neural correlates of actual inhibition revealed the activation of IFG specifically (Aron et al., <xref rid="B3" ref-type="bibr">2004</xref>; Collette et al., <xref rid="B8" ref-type="bibr">2006</xref>; Vidal et al., <xref rid="B63" ref-type="bibr">2012</xref>). For switch, several studies found that it was associated with the DLPFC broadly (Collette et al., <xref rid="B8" ref-type="bibr">2006</xref>; Jazbec et al., <xref rid="B28" ref-type="bibr">2007</xref>). Moreover, the different cortical areas of PFC are activated during different stages of the switch task (Oh et al., <xref rid="B44" ref-type="bibr">2014</xref>). As the right MFG (BA 46) was also associated with planning and working memory (see above), the middle DLPFC might be a crucial area for the information integration and controlling of EF (Petrides, <xref rid="B49" ref-type="bibr">2005</xref>). The discrepancy between our resting-state findings and the existing task-evoked findings may be due to multiple reasons, such as the differences in research foci (our study's focus on network properties vs. the existing research's focus on neural activations) and the task demands (resting-state vs. performing a specific task). Future specifically designed studies could address this issue by linking the resting-state network properties to both EF behavioral performance and EF task related activations.</p></sec><sec><title>Limitation and conclusion</title><p>Despite our novel findings, the limitations should be taken into consideration. First, in this study, we only assessed the network properties in the PFC regions. Although our findings are in line with the existing studies that suggest the central role of the prefrontal cortex (PFC) in EF, recent studies suggested that some other regions, such as parietal cortex are also involved in EF. Thus, future studies may wish to extend the range of a similar fNIRS study to cover the whole cortical surface. Second, intelligence was not included in this study. It is generally accepted that intelligence is related to EF (Friedman et al., <xref rid="B21" ref-type="bibr">2006</xref>; Duan et al., <xref rid="B17" ref-type="bibr">2010</xref>) but the relation between them in the brain is less clear. Hence, it is necessary to introduce intelligence as a control variable to take into consideration the effect of intelligence on EF performance and its relation to the neural network topological properties. Third, changes in functional connectivity between two regions may also be affected by changes in signal or noise in either region or by another connection (Friston, <xref rid="B23" ref-type="bibr">2011</xref>). Thus, future studies must use stricter experimental controls, a larger sample size, and more advanced statistical methods to rule out the influence of these potentially confounding factors. For example, future work employing a executive function task could utilize psychophysiological interaction analysis, which reveals consistent and specific patterns of effective connectivity (e.g., Smith et al., <xref rid="B54" ref-type="bibr">2016</xref>). Forth, on one hand, the limited spatial resolution of fNIRS compared to fMRI may result in the less accurate space localization of the cortex regions. On the other hand, the superior temporal resolution of fNIRS is more conducive to describe the time course of the resting state fluctuations of the brain's neural activities. Thus, to study the resting-state neural correlation of EF, future studies should consider combining fNIRS with fMRI.</p><p>This study investigated the relationship between the behavioral EF scores and the resting-state functional network topological properties in the PFC utilizing functional near infrared spectroscopy (fNIRS). We found that only regional nodal but not global network properties were associated with EF components scores. We also observed that the different EF components were related to different regional properties in various PFC areas. Our findings suggested that the resting-state PFC activity plays an important role in individuals' behavioral performance in the executive function tasks. Further, the resting-state functional network can reveal the intrinsic neural mechanisms underlying behavioral EF abilities.</p></sec></sec><sec id="s5"><title>Author contributions</title><p>Experimental design: JL, GF, KL, and XD. Experimental data recording: JZ, GZ, XJ, and GC. Experimental data analyze: JZ and JL. Manuscript writing: JZ and JL. Manuscript revision: JL and KL.</p><sec><title>Conflict of interest statement</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></sec></body><back><ack><p>This paper is supported by the National Natural Science Foundation of China under Grant No. 61375110, 61673051, 31470993, 31371041 the Fundamental Research Funds for the Central Universities (2015JBM031), the NIH (R01HD046526) and the Natural Sciences and Engineering Research Council of Canada.</p></ack><ref-list><title>References</title><ref id="B1"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Achard</surname><given-names>S.</given-names></name><name><surname>Bullmore</surname><given-names>E.</given-names></name></person-group> (<year>2007</year>). <article-title>Efficiency and cost of economical brain functional networks</article-title>. <source>PLoS Comput. Biol.</source><volume>3</volume>:<fpage>e17</fpage>. <pub-id pub-id-type="doi">10.1371/journal.pcbi.0030017</pub-id><pub-id pub-id-type="pmid">17274684</pub-id></mixed-citation></ref><ref id="B2"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Achard</surname><given-names>S.</given-names></name><name><surname>Salvador</surname><given-names>R.</given-names></name><name><surname>Whitcher</surname><given-names>B.</given-names></name><name><surname>Suckling</surname><given-names>J.</given-names></name><name><surname>Bullmore</surname><given-names>E. D.</given-names></name></person-group> (<year>2006</year>). <article-title>A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs</article-title>. <source>J. Neurosci.</source><volume>26</volume>, <fpage>63</fpage>–<lpage>72</lpage>. <pub-id pub-id-type="doi">10.1523/JNEUROSCI.3874-05.2006</pub-id><pub-id pub-id-type="pmid">16399673</pub-id></mixed-citation></ref><ref id="B3"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Aron</surname><given-names>A. R.</given-names></name><name><surname>Robbins</surname><given-names>T. W.</given-names></name><name><surname>Poldrack</surname><given-names>R. A.</given-names></name></person-group> (<year>2004</year>). <article-title>Inhibition and the right inferior frontal cortex</article-title>. <source>Trends Cogn. Sci.</source><volume>8</volume>, <fpage>170</fpage>–<lpage>177</lpage>. <pub-id pub-id-type="doi">10.1016/j.tics.2004.02.010</pub-id><pub-id pub-id-type="pmid">15050513</pub-id></mixed-citation></ref><ref id="B4"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Biswal</surname><given-names>B.</given-names></name><name><surname>Zerrin Yetkin</surname><given-names>F.</given-names></name><name><surname>Haughton</surname><given-names>V. M.</given-names></name><name><surname>Hyde</surname><given-names>J. S.</given-names></name></person-group> (<year>1995</year>). <article-title>Functional connectivity in the motor cortex of resting human brain using echo-planar MRI</article-title>. <source>Magn. Reson. Med.</source><volume>34</volume>, <fpage>537</fpage>–<lpage>541</lpage>. <pub-id pub-id-type="doi">10.1002/mrm.1910340409</pub-id><pub-id pub-id-type="pmid">8524021</pub-id></mixed-citation></ref><ref id="B5"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Boas</surname><given-names>D. A.</given-names></name><name><surname>Elwell</surname><given-names>C. E.</given-names></name><name><surname>Ferrari</surname><given-names>M.</given-names></name><name><surname>Taga</surname><given-names>G.</given-names></name></person-group> (<year>2014</year>). <article-title>Twenty years of functional near-infrared spectroscopy: introduction for the special issue</article-title>. <source>Neuroimage</source><volume>85</volume>, <fpage>1</fpage>–<lpage>5</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroimage.2013.11.033</pub-id><pub-id pub-id-type="pmid">24321364</pub-id></mixed-citation></ref><ref id="B6"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Bonnì</surname><given-names>S.</given-names></name><name><surname>Perri</surname><given-names>R.</given-names></name><name><surname>Fadda</surname><given-names>L.</given-names></name><name><surname>Tomaiuolo</surname><given-names>F.</given-names></name><name><surname>Koch</surname><given-names>G.</given-names></name><name><surname>Caltagirone</surname><given-names>C.</given-names></name><etal></etal></person-group>. (<year>2014</year>). <article-title>Selective deficit of spatial short-term memory: role of storage and rehearsal mechanisms</article-title>. <source>Cortex</source><volume>59</volume>, <fpage>22</fpage>–<lpage>32</lpage>. <pub-id pub-id-type="doi">10.1016/j.cortex.2014.06.004</pub-id><pub-id pub-id-type="pmid">25127485</pub-id></mixed-citation></ref><ref id="B7"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Burgess</surname><given-names>P. W.</given-names></name><name><surname>Veitch</surname><given-names>E.</given-names></name><name><surname>de Lacy Costello</surname><given-names>A.</given-names></name><name><surname>Shallice</surname><given-names>T.</given-names></name></person-group> (<year>2000</year>). <article-title>The cognitive and neuroanatomical correlates of multitasking</article-title>. <source>Neuropsychologia</source><volume>38</volume>, <fpage>848</fpage>–<lpage>863</lpage>. <pub-id pub-id-type="doi">10.1016/S0028-3932(99)00134-7</pub-id><pub-id pub-id-type="pmid">10689059</pub-id></mixed-citation></ref><ref id="B8"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Collette</surname><given-names>F.</given-names></name><name><surname>Hogge</surname><given-names>M.</given-names></name><name><surname>Salmon</surname><given-names>E.</given-names></name><name><surname>Van der Linden</surname><given-names>M.</given-names></name></person-group> (<year>2006</year>). <article-title>Exploration of the neural substrates of executive functioning by functional neuroimaging</article-title>. <source>Neuroscience</source><volume>139</volume>, <fpage>209</fpage>–<lpage>221</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroscience.2005.05.035</pub-id><pub-id pub-id-type="pmid">16324796</pub-id></mixed-citation></ref><ref id="B9"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Collinson</surname><given-names>S. L.</given-names></name><name><surname>Tong</surname><given-names>S. J.</given-names></name><name><surname>Loh</surname><given-names>S. S.</given-names></name><name><surname>Chionh</surname><given-names>S. B.</given-names></name><name><surname>Merchant</surname><given-names>R. A.</given-names></name></person-group> (<year>2014</year>). <article-title>Midlife metabolic syndrome and neurocognitive function in a mixed Asian sample</article-title>. <source>Int. Psychogeriatr.</source><volume>26</volume>, <fpage>1305</fpage>–<lpage>1316</lpage>. <pub-id pub-id-type="doi">10.1017/S104161021400057X</pub-id><pub-id pub-id-type="pmid">24787730</pub-id></mixed-citation></ref><ref id="B10"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Cui</surname><given-names>X.</given-names></name><name><surname>Bray</surname><given-names>S.</given-names></name><name><surname>Bryant</surname><given-names>D. M.</given-names></name><name><surname>Glover</surname><given-names>G. H.</given-names></name><name><surname>Reiss</surname><given-names>A. L.</given-names></name></person-group> (<year>2011</year>). <article-title>A quantitative comparison of nirs and fmri across multiple cognitive tasks</article-title>. <source>Neuroimage</source><volume>54</volume>, <fpage>2808</fpage>–<lpage>2821</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroimage.2010.10.069</pub-id><pub-id pub-id-type="pmid">21047559</pub-id></mixed-citation></ref><ref id="B11"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Damoiseaux</surname><given-names>J. S.</given-names></name><name><surname>Beckmann</surname><given-names>C. F.</given-names></name><name><surname>Arigita</surname><given-names>E. S.</given-names></name><name><surname>Barkhof</surname><given-names>F.</given-names></name><name><surname>Scheltens</surname><given-names>P.</given-names></name><name><surname>Stam</surname><given-names>C. J.</given-names></name><etal></etal></person-group>. (<year>2008</year>). <article-title>Reduced resting-state brain activity in the “default network” in normal aging</article-title>. <source>Cereb. Cortex</source><volume>18</volume>, <fpage>1856</fpage>–<lpage>1864</lpage>. <pub-id pub-id-type="doi">10.1093/cercor/bhm207</pub-id><pub-id pub-id-type="pmid">18063564</pub-id></mixed-citation></ref><ref id="B12"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Davidson</surname><given-names>R. J.</given-names></name><name><surname>Abercrombie</surname><given-names>H.</given-names></name><name><surname>Nitschke</surname><given-names>J. B.</given-names></name><name><surname>Putnam</surname><given-names>K.</given-names></name></person-group> (<year>1999</year>). <article-title>Regional brain function, emotion and disorders of emotion</article-title>. <source>Curr. Opin. Neurobiol.</source><volume>9</volume>, <fpage>228</fpage>–<lpage>234</lpage>. <pub-id pub-id-type="doi">10.1016/S0959-4388(99)80032-4</pub-id><pub-id pub-id-type="pmid">10322186</pub-id></mixed-citation></ref><ref id="B13"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>De Luca</surname><given-names>C. R.</given-names></name><name><surname>Wood</surname><given-names>S. J.</given-names></name><name><surname>Anderson</surname><given-names>V.</given-names></name><name><surname>Buchanan</surname><given-names>J. A.</given-names></name><name><surname>Proffitt</surname><given-names>T. M.</given-names></name><name><surname>Mahony</surname><given-names>K.</given-names></name><etal></etal></person-group>. (<year>2003</year>). <article-title>Normative data from the CANTAB. I: development of executive function over the lifespan</article-title>. <source>J. Clin. Exp. Neuropsychol.</source><volume>25</volume>, <fpage>242</fpage>–<lpage>254</lpage>. <pub-id pub-id-type="doi">10.1076/jcen.25.2.242.13639</pub-id><pub-id pub-id-type="pmid">12754681</pub-id></mixed-citation></ref><ref id="B14"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>de Vries</surname><given-names>F. E.</given-names></name><name><surname>de Wit</surname><given-names>S. J.</given-names></name><name><surname>Cath</surname><given-names>D. C.</given-names></name><name><surname>van der Werf</surname><given-names>Y. D.</given-names></name><name><surname>van der Borden</surname><given-names>V.</given-names></name><name><surname>van Rossum</surname><given-names>T. B.</given-names></name><etal></etal></person-group>. (<year>2014</year>). <article-title>Compensatory frontoparietal activity during working memory: an endophenotype of Obsessive-Compulsive Disorder</article-title>. <source>Biol. Psychiatry</source><volume>76</volume>, <fpage>878</fpage>–<lpage>887</lpage>. <pub-id pub-id-type="doi">10.1016/j.biopsych.2013.11.021</pub-id><pub-id pub-id-type="pmid">24365484</pub-id></mixed-citation></ref><ref id="B15"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ding</surname><given-names>X. P.</given-names></name><name><surname>Gao</surname><given-names>X.</given-names></name><name><surname>Fu</surname><given-names>G.</given-names></name><name><surname>Lee</surname><given-names>K.</given-names></name></person-group> (<year>2013</year>). <article-title>Neural correlates of spontaneous deception: a functional near-infrared spectroscopy (fNIRS) study</article-title>. <source>Neuropsychologia</source><volume>51</volume>, <fpage>704</fpage>–<lpage>712</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuropsychologia.2012.12.018</pub-id><pub-id pub-id-type="pmid">23340482</pub-id></mixed-citation></ref><ref id="B16"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Douw</surname><given-names>L.</given-names></name><name><surname>Schoonheim</surname><given-names>M. M.</given-names></name><name><surname>Landi</surname><given-names>D.</given-names></name><name><surname>van der Meer</surname><given-names>M. L.</given-names></name><name><surname>Geurts</surname><given-names>J. J.</given-names></name><name><surname>Reijneveld</surname><given-names>J. C.</given-names></name><etal></etal></person-group>. (<year>2011</year>). <article-title>Cognition is related to resting-state small-world network topology: a magnetoencephalographic study</article-title>. <source>Neuroscience</source><volume>175</volume>, <fpage>169</fpage>–<lpage>177</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroscience.2010.11.039</pub-id><pub-id pub-id-type="pmid">21130847</pub-id></mixed-citation></ref><ref id="B17"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Duan</surname><given-names>X.</given-names></name><name><surname>Wei</surname><given-names>S.</given-names></name><name><surname>Wang</surname><given-names>G.</given-names></name><name><surname>Shi</surname><given-names>J.</given-names></name></person-group> (<year>2010</year>). <article-title>The relationship between executive function and intelligence on 11- to 12-year-old children</article-title>. <source>Psychol. Test Assess. Model.</source><volume>52</volume>, <fpage>419</fpage>–<lpage>431</lpage>.</mixed-citation></ref><ref id="B18"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ferrari</surname><given-names>M.</given-names></name><name><surname>Quaresima</surname><given-names>V.</given-names></name></person-group> (<year>2012</year>). <article-title>A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application</article-title>. <source>Neuroimage</source><volume>63</volume>, <fpage>921</fpage>–<lpage>935</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroimage.2012.03.049</pub-id><pub-id pub-id-type="pmid">22510258</pub-id></mixed-citation></ref><ref id="B19"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Fox</surname><given-names>M. D.</given-names></name><name><surname>Snyder</surname><given-names>A. Z.</given-names></name><name><surname>Vincent</surname><given-names>J. L.</given-names></name><name><surname>Corbetta</surname><given-names>M.</given-names></name><name><surname>Van Essen</surname><given-names>D. C.</given-names></name><name><surname>Raichle</surname><given-names>M. E.</given-names></name></person-group> (<year>2005</year>). <article-title>The human brain is intrinsically organized into dynamic, anticorrelated functional networks</article-title>. <source>Proc. Natl. Acad. Sci. U.S.A.</source><volume>102</volume>, <fpage>9673</fpage>–<lpage>9678</lpage>. <pub-id pub-id-type="doi">10.1073/pnas.0504136102</pub-id><pub-id pub-id-type="pmid">15976020</pub-id></mixed-citation></ref><ref id="B20"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Freeman</surname><given-names>L. C.</given-names></name></person-group> (<year>1977</year>). <article-title>A set of measures of centrality based on betweenness</article-title>. <source>Sociometry</source><volume>40</volume>, <fpage>35</fpage>–<lpage>41</lpage>. <pub-id pub-id-type="doi">10.2307/3033543</pub-id></mixed-citation></ref><ref id="B21"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Friedman</surname><given-names>N. P.</given-names></name><name><surname>Miyake</surname><given-names>A.</given-names></name><name><surname>Corley</surname><given-names>R. P.</given-names></name><name><surname>Young</surname><given-names>S. E.</given-names></name><name><surname>Defries</surname><given-names>J. C.</given-names></name><name><surname>Hewitt</surname><given-names>J. K.</given-names></name></person-group> (<year>2006</year>). <article-title>Not all executive functions are related to intelligence</article-title>. <source>Psychol. Sci.</source><volume>17</volume>, <fpage>172</fpage>–<lpage>179</lpage>. <pub-id pub-id-type="doi">10.1111/j.1467-9280.2006.01681.x</pub-id><pub-id pub-id-type="pmid">16466426</pub-id></mixed-citation></ref><ref id="B22"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Friston</surname><given-names>K. J.</given-names></name></person-group> (<year>1994</year>). <article-title>Functional and effective connectivity in neuroimaging: a synthesis</article-title>. <source>Hum. Brain Mapp.</source><volume>2</volume>, <fpage>56</fpage>–<lpage>78</lpage>. <pub-id pub-id-type="doi">10.1002/hbm.460020107</pub-id></mixed-citation></ref><ref id="B23"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Friston</surname><given-names>K. J.</given-names></name></person-group> (<year>2011</year>). <article-title>Functional and effective connectivity: a review</article-title>. <source>Brain Connect.</source><volume>1</volume>, <fpage>13</fpage>–<lpage>36</lpage>. <pub-id pub-id-type="doi">10.1089/brain.2011.0008</pub-id><pub-id pub-id-type="pmid">22432952</pub-id></mixed-citation></ref><ref id="B24"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Gao</surname><given-names>Q.</given-names></name><name><surname>Xu</surname><given-names>Q.</given-names></name><name><surname>Duan</surname><given-names>X.</given-names></name><name><surname>Liao</surname><given-names>W.</given-names></name><name><surname>Ding</surname><given-names>J.</given-names></name><name><surname>Zhang</surname><given-names>Z.</given-names></name><etal></etal></person-group>. (<year>2013</year>). <article-title>Extraversion and neuroticism relate to topological properties of resting-state brain networks</article-title>. <source>Front. Hum. Neurosci.</source><volume>7</volume>:<issue>257</issue>. <pub-id pub-id-type="doi">10.3389/fnhum.2013.00257</pub-id><pub-id pub-id-type="pmid">23781183</pub-id></mixed-citation></ref><ref id="B25"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Gau</surname><given-names>S. S. F.</given-names></name><name><surname>Shang</surname><given-names>C. Y.</given-names></name></person-group> (<year>2010</year>). <article-title>Executive functions as endophenotypes in ADHD: evidence from the Cambridge Neuropsychological Test Battery (CANTAB)</article-title>. <source>J. Child Psychol. Psychiatry</source><volume>51</volume>, <fpage>838</fpage>–<lpage>849</lpage>. <pub-id pub-id-type="doi">10.1111/j.1469-7610.2010.02215.x</pub-id><pub-id pub-id-type="pmid">20085608</pub-id></mixed-citation></ref><ref id="B26"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Gu</surname><given-names>B. M.</given-names></name><name><surname>Park</surname><given-names>J. Y.</given-names></name><name><surname>Kang</surname><given-names>D. H.</given-names></name><name><surname>Lee</surname><given-names>S. J.</given-names></name><name><surname>Yoo</surname><given-names>S. Y.</given-names></name><name><surname>Jo</surname><given-names>H. J.</given-names></name><etal></etal></person-group>. (<year>2008</year>). <article-title>Neural correlates of cognitive inflexibility during task-switching in Obsessive-Compulsive Disorder</article-title>. <source>Brain</source><volume>131</volume>, <fpage>155</fpage>–<lpage>164</lpage>. <pub-id pub-id-type="doi">10.1093/brain/awm277</pub-id><pub-id pub-id-type="pmid">18065438</pub-id></mixed-citation></ref><ref id="B27"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Homae</surname><given-names>F.</given-names></name><name><surname>Watanabe</surname><given-names>H.</given-names></name><name><surname>Nakano</surname><given-names>T.</given-names></name><name><surname>Taga</surname><given-names>G.</given-names></name></person-group> (<year>2007</year>). <article-title>Prosodic processing in the developing brain</article-title>. <source>Neurosci. Res.</source><volume>59</volume>, <fpage>29</fpage>–<lpage>39</lpage>. <pub-id pub-id-type="doi">10.1016/j.neures.2007.05.005</pub-id><pub-id pub-id-type="pmid">17590470</pub-id></mixed-citation></ref><ref id="B28"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Jazbec</surname><given-names>S.</given-names></name><name><surname>Pantelis</surname><given-names>C.</given-names></name><name><surname>Robbins</surname><given-names>T.</given-names></name><name><surname>Weickert</surname><given-names>T.</given-names></name><name><surname>Weinberger</surname><given-names>D. R.</given-names></name><name><surname>Goldberg</surname><given-names>T. E.</given-names></name></person-group> (<year>2007</year>). <article-title>Intra-dimensional/extra-dimensional set-shifting performance in Schizophrenia: impact of distractors</article-title>. <source>Schizophr. Res.</source><volume>89</volume>, <fpage>339</fpage>–<lpage>349</lpage>. <pub-id pub-id-type="doi">10.1016/j.schres.2006.08.014</pub-id><pub-id pub-id-type="pmid">17055703</pub-id></mixed-citation></ref><ref id="B29"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Langer</surname><given-names>N.</given-names></name><name><surname>Pedroni</surname><given-names>A.</given-names></name><name><surname>Gianotti</surname><given-names>L. R.</given-names></name><name><surname>Hänggi</surname><given-names>J.</given-names></name><name><surname>Knoch</surname><given-names>D.</given-names></name><name><surname>Jäncke</surname><given-names>L.</given-names></name></person-group> (<year>2012</year>). <article-title>Functional brain network efficiency predicts intelligence</article-title>. <source>Hum. Brain Mapp.</source><volume>33</volume>, <fpage>1393</fpage>–<lpage>1406</lpage>. <pub-id pub-id-type="doi">10.1002/hbm.21297</pub-id><pub-id pub-id-type="pmid">21557387</pub-id></mixed-citation></ref><ref id="B30"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Latora</surname><given-names>V.</given-names></name><name><surname>Marchiori</surname><given-names>M.</given-names></name></person-group> (<year>2001</year>). <article-title>Efficient behavior of small-world networks</article-title>. <source>Phys. Rev. Lett.</source><volume>87</volume>:<fpage>198701</fpage>. <pub-id pub-id-type="doi">10.1103/PhysRevLett.87.198701</pub-id><pub-id pub-id-type="pmid">11690461</pub-id></mixed-citation></ref><ref id="B31"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lee</surname><given-names>M. H.</given-names></name><name><surname>Smyser</surname><given-names>C. D.</given-names></name><name><surname>Shimony</surname><given-names>J. S.</given-names></name></person-group> (<year>2013</year>). <article-title>Resting-state fMRI: a review of methods and clinical applications</article-title>. <source>Am. J. Neuroradiol.</source><volume>34</volume>, <fpage>1866</fpage>–<lpage>1872</lpage>. <pub-id pub-id-type="doi">10.3174/ajnr.A3263</pub-id><pub-id pub-id-type="pmid">22936095</pub-id></mixed-citation></ref><ref id="B32"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Li</surname><given-names>Y.</given-names></name><name><surname>Liu</surname><given-names>Y.</given-names></name><name><surname>Li</surname><given-names>J.</given-names></name><name><surname>Qin</surname><given-names>W.</given-names></name><name><surname>Li</surname><given-names>K.</given-names></name><name><surname>Yu</surname><given-names>C.</given-names></name><etal></etal></person-group>. (<year>2009</year>). <article-title>Brain anatomical network and intelligence</article-title>. <source>PLoS Comput. Biol.</source><volume>5</volume>:<fpage>e1000395</fpage>. <pub-id pub-id-type="doi">10.1371/journal.pcbi.1000395</pub-id><pub-id pub-id-type="pmid">19492086</pub-id></mixed-citation></ref><ref id="B33"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Li</surname><given-names>Z.</given-names></name><name><surname>Liu</surname><given-names>H.</given-names></name><name><surname>Liao</surname><given-names>X.</given-names></name><name><surname>Xu</surname><given-names>J.</given-names></name><name><surname>Liu</surname><given-names>W.</given-names></name><name><surname>Tian</surname><given-names>F.</given-names></name><etal></etal></person-group>. (<year>2015</year>). <article-title>Dynamic functional connectivity revealed by resting-state functional near-infrared spectroscopy</article-title>. <source>Biomed. Opt. Express</source><volume>6</volume>, <fpage>2337</fpage>–<lpage>2352</lpage>. <pub-id pub-id-type="doi">10.1364/BOE.6.002337</pub-id><pub-id pub-id-type="pmid">26203365</pub-id></mixed-citation></ref><ref id="B34"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lin</surname><given-names>H. Y.</given-names></name><name><surname>Tseng</surname><given-names>W. Y. I.</given-names></name><name><surname>Lai</surname><given-names>M. C.</given-names></name><name><surname>Matsuo</surname><given-names>K.</given-names></name><name><surname>Gau</surname><given-names>S. S. F.</given-names></name></person-group> (<year>2015</year>). <article-title>Altered Resting-State Frontoparietal Control Network in Children with Attention-Deficit/Hyperactivity Disorder</article-title>. <source>J. Int. Neuropsychol. Soc.</source><volume>21</volume>, <fpage>1</fpage>–<lpage>14</lpage>. <pub-id pub-id-type="doi">10.1017/s135561771500020x</pub-id><pub-id pub-id-type="pmid">25399546</pub-id></mixed-citation></ref><ref id="B35"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lu</surname><given-names>C. M.</given-names></name><name><surname>Zhang</surname><given-names>Y. J.</given-names></name><name><surname>Biswal</surname><given-names>B. B.</given-names></name><name><surname>Zang</surname><given-names>Y. F.</given-names></name><name><surname>Peng</surname><given-names>D. L.</given-names></name><name><surname>Zhu</surname><given-names>C. Z.</given-names></name></person-group> (<year>2010</year>). <article-title>Use of fNIRS to assess resting state functional connectivity</article-title>. <source>J. Neurosci. Methods</source><volume>186</volume>, <fpage>242</fpage>–<lpage>249</lpage>. <pub-id pub-id-type="doi">10.1016/j.jneumeth.2009.11.010</pub-id><pub-id pub-id-type="pmid">19931310</pub-id></mixed-citation></ref><ref id="B36"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Maslov</surname><given-names>S.</given-names></name><name><surname>Sneppen</surname><given-names>K.</given-names></name></person-group> (<year>2002</year>). <article-title>Specificity and stability in topology of protein networks</article-title>. <source>Science</source><volume>296</volume>, <fpage>910</fpage>–<lpage>913</lpage>. <pub-id pub-id-type="doi">10.1126/science.1065103</pub-id><pub-id pub-id-type="pmid">11988575</pub-id></mixed-citation></ref><ref id="B37"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Melloni</surname><given-names>M.</given-names></name><name><surname>Urbistondo</surname><given-names>C.</given-names></name><name><surname>Sedeño</surname><given-names>L.</given-names></name><name><surname>Gelormini</surname><given-names>C.</given-names></name><name><surname>Kichic</surname><given-names>R.</given-names></name><name><surname>Ibanez</surname><given-names>A.</given-names></name></person-group> (<year>2012</year>). <article-title>The extended fronto-striatal model of Obsessive Compulsive Disorder: convergence from event-related potentials, neuropsychology and neuroimaging</article-title>. <source>Front. Hum. Neurosci.</source><volume>6</volume>:<issue>259</issue>. <pub-id pub-id-type="doi">10.3389/fnhum.2012.00259</pub-id><pub-id pub-id-type="pmid">23015786</pub-id></mixed-citation></ref><ref id="B38"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Miyasaki</surname><given-names>J. M.</given-names></name><name><surname>Shannon</surname><given-names>K.</given-names></name><name><surname>Voon</surname><given-names>V.</given-names></name><name><surname>Ravina</surname><given-names>B.</given-names></name><name><surname>Kleiner-Fisman</surname><given-names>G.</given-names></name><name><surname>Anderson</surname><given-names>K.</given-names></name><etal></etal></person-group>. (<year>2006</year>). <article-title>Practice parameter: evaluation and treatment of depression, psychosis, and dementia in Parkinson disease (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology</article-title>. <source>Neurology</source><volume>66</volume>, <fpage>996</fpage>–<lpage>1002</lpage>. <pub-id pub-id-type="doi">10.3389/fnhum.2012.00259</pub-id><pub-id pub-id-type="pmid">16606910</pub-id></mixed-citation></ref><ref id="B39"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Moriguchi</surname><given-names>Y.</given-names></name><name><surname>Hiraki</surname><given-names>K.</given-names></name></person-group> (<year>2013</year>). <article-title>Prefrontal cortex and executive function in young children: a review of NIRS studies</article-title>. <source>Front. Hum. Neurosci.</source><volume>7</volume>:<issue>867</issue>. <pub-id pub-id-type="doi">10.3389/fnhum.2013.00867</pub-id><pub-id pub-id-type="pmid">24381551</pub-id></mixed-citation></ref><ref id="B40"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Newman</surname><given-names>M. E. J.</given-names></name></person-group> (<year>2003</year>). <article-title>The structure and function of complex networks</article-title>. <source>SIAM Revi.</source><volume>45</volume>, <fpage>167</fpage>–<lpage>256</lpage>. <pub-id pub-id-type="doi">10.1137/S003614450342480</pub-id></mixed-citation></ref><ref id="B41"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Newman</surname><given-names>S. D.</given-names></name><name><surname>Carpenter</surname><given-names>P. A.</given-names></name><name><surname>Varma</surname><given-names>S.</given-names></name><name><surname>Just</surname><given-names>M. A.</given-names></name></person-group> (<year>2003</year>). <article-title>Frontal and parietal participation in problem solving in the Tower of London: fMRI and computational modeling of planning and high-level perception</article-title>. <source>Neuropsychologia</source><volume>41</volume>, <fpage>1668</fpage>–<lpage>1682</lpage>. <pub-id pub-id-type="doi">10.1016/S0028-3932(03)00091-5</pub-id><pub-id pub-id-type="pmid">12887991</pub-id></mixed-citation></ref><ref id="B42"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Niu</surname><given-names>H.</given-names></name><name><surname>He</surname><given-names>Y.</given-names></name></person-group> (<year>2014</year>). <article-title>Resting-state functional brain connectivity: lessons from functional near-Infrared spectroscopy</article-title>. <source>Neuroscientist</source>. <volume>20</volume>, <fpage>173</fpage>–<lpage>188</lpage>. <pub-id pub-id-type="doi">10.1177/1073858413502707</pub-id><pub-id pub-id-type="pmid">24022325</pub-id></mixed-citation></ref><ref id="B43"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Niu</surname><given-names>H.</given-names></name><name><surname>Wang</surname><given-names>J.</given-names></name><name><surname>Zhao</surname><given-names>T.</given-names></name><name><surname>Shu</surname><given-names>N.</given-names></name><name><surname>He</surname><given-names>Y.</given-names></name></person-group> (<year>2012</year>). <article-title>Revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy</article-title>. <source>PLoS ONE</source><volume>7</volume>:<fpage>e45771</fpage>. <pub-id pub-id-type="doi">10.1371/journal.pone.0045771</pub-id><pub-id pub-id-type="pmid">23029235</pub-id></mixed-citation></ref><ref id="B44"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Oh</surname><given-names>A.</given-names></name><name><surname>Vidal</surname><given-names>J.</given-names></name><name><surname>Taylor</surname><given-names>M. J.</given-names></name><name><surname>Pang</surname><given-names>E. W.</given-names></name></person-group> (<year>2014</year>). <article-title>Neuromagnetic correlates of intra- and extra-dimensional set-shifting</article-title>. <source>Brain Cogn.</source><volume>86</volume>, <fpage>90</fpage>–<lpage>97</lpage>. <pub-id pub-id-type="doi">10.1016/j.bandc.2014.02.006</pub-id><pub-id pub-id-type="pmid">24607731</pub-id></mixed-citation></ref><ref id="B45"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Owen</surname><given-names>A. M.</given-names></name><name><surname>Downes</surname><given-names>J. J.</given-names></name><name><surname>Sahakian</surname><given-names>B. J.</given-names></name><name><surname>Polkey</surname><given-names>C. E.</given-names></name><name><surname>Robbins</surname><given-names>T. W.</given-names></name></person-group> (<year>1990</year>). <article-title>Planning and spatial working memory following frontal lobe lesions in man</article-title>. <source>Neuropsychologia</source><volume>28</volume>, <fpage>1021</fpage>–<lpage>1034</lpage>. <pub-id pub-id-type="doi">10.1016/0028-3932(90)90137-D</pub-id><pub-id pub-id-type="pmid">2267054</pub-id></mixed-citation></ref><ref id="B46"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Owen</surname><given-names>A. M.</given-names></name><name><surname>Roberts</surname><given-names>A. C.</given-names></name><name><surname>Polkey</surname><given-names>C. E.</given-names></name><name><surname>Sahakian</surname><given-names>B. J.</given-names></name><name><surname>Robbins</surname><given-names>T. W.</given-names></name></person-group> (<year>1991</year>). <article-title>Extra-dimensional versus intra-dimensional set shifting performance following frontal lobe excisions, temporal lobe excisions or amygdalo-hippocampectomy in man</article-title>. <source>Neuropsychologia</source><volume>29</volume>, <fpage>993</fpage>–<lpage>1006</lpage>. <pub-id pub-id-type="doi">10.1016/0028-3932(91)90063-E</pub-id><pub-id pub-id-type="pmid">1762678</pub-id></mixed-citation></ref><ref id="B47"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ozonoff</surname><given-names>S.</given-names></name><name><surname>Cook</surname><given-names>I.</given-names></name><name><surname>Coon</surname><given-names>H.</given-names></name><name><surname>Dawson</surname><given-names>G.</given-names></name><name><surname>Joseph</surname><given-names>R. M.</given-names></name><name><surname>Klin</surname><given-names>A.</given-names></name><etal></etal></person-group>. (<year>2004</year>). <article-title>Performance on cambridge neuropsychological test automated battery subtests sensitive to frontal lobe function in people with autistic disorder: evidence from the collaborative programs of excellence in autism network</article-title>. <source>J. Autism Dev. Disord.</source><volume>34</volume>, <fpage>139</fpage>–<lpage>150</lpage>. <pub-id pub-id-type="doi">10.1023/B:JADD.0000022605.81989.cc</pub-id><pub-id pub-id-type="pmid">15162933</pub-id></mixed-citation></ref><ref id="B48"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Peters</surname><given-names>F.</given-names></name><name><surname>Collette</surname><given-names>F.</given-names></name><name><surname>Degueldre</surname><given-names>C.</given-names></name><name><surname>Sterpenich</surname><given-names>V.</given-names></name><name><surname>Majerus</surname><given-names>S.</given-names></name><name><surname>Salmon</surname><given-names>E.</given-names></name></person-group> (<year>2009</year>). <article-title>The neural correlates of verbal short-term memory in Alzheimer's disease: an fMRI study</article-title>. <source>Brain</source><volume>132</volume>, <fpage>1833</fpage>–<lpage>1846</lpage>. <pub-id pub-id-type="doi">10.1093/brain/awp075</pub-id><pub-id pub-id-type="pmid">19433442</pub-id></mixed-citation></ref><ref id="B49"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Petrides</surname><given-names>M.</given-names></name></person-group> (<year>2005</year>). <article-title>Lateral prefrontal cortex: architectonic and functional organization</article-title>. <source>Philos. Trans. R. Soc. B Biol. Sci.</source><volume>360</volume>, <fpage>781</fpage>–<lpage>795</lpage>. <pub-id pub-id-type="doi">10.1098/rstb.2005.1631</pub-id><pub-id pub-id-type="pmid">15937012</pub-id></mixed-citation></ref><ref id="B50"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Pu</surname><given-names>S.</given-names></name><name><surname>Yamada</surname><given-names>T.</given-names></name><name><surname>Yokoyama</surname><given-names>K.</given-names></name><name><surname>Matsumura</surname><given-names>H.</given-names></name><name><surname>Kobayashi</surname><given-names>H.</given-names></name><name><surname>Sasaki</surname><given-names>N.</given-names></name><etal></etal></person-group>. (<year>2011</year>). <article-title>A multi-channel near-infrared spectroscopy study of prefrontal cortex activation during working memory task in major depressive disorder</article-title>. <source>Neurosci. Res.</source><volume>70</volume>, <fpage>91</fpage>–<lpage>97</lpage>. <pub-id pub-id-type="doi">10.1016/j.neures.2011.01.001</pub-id><pub-id pub-id-type="pmid">21241745</pub-id></mixed-citation></ref><ref id="B51"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Reineberg</surname><given-names>A. E.</given-names></name><name><surname>Andrews-Hanna</surname><given-names>J. R.</given-names></name><name><surname>Depue</surname><given-names>B. E.</given-names></name><name><surname>Friedman</surname><given-names>N. P.</given-names></name><name><surname>Banich</surname><given-names>M. T.</given-names></name></person-group> (<year>2015</year>). <article-title>Resting-state networks predict individual differences in common and specific aspects of executive function</article-title>. <source>Neuroimage</source><volume>104</volume>, <fpage>69</fpage>–<lpage>78</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroimage.2014.09.045</pub-id><pub-id pub-id-type="pmid">25281800</pub-id></mixed-citation></ref><ref id="B52"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Selemon</surname><given-names>L. D.</given-names></name><name><surname>Rajkowska</surname><given-names>G.</given-names></name><name><surname>Goldman-Rakic</surname><given-names>P. S.</given-names></name></person-group> (<year>1998</year>). <article-title>Elevated neuronal density in prefrontal area 46 in brains from schizophrenic patients: application of a three-dimensional, stereologic counting method</article-title>. <source>J. Comp. Neurol.</source><volume>392</volume>, <fpage>402</fpage>–<lpage>412</lpage>. <pub-id pub-id-type="pmid">9511926</pub-id></mixed-citation></ref><ref id="B53"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Singh</surname><given-names>A. K.</given-names></name><name><surname>Okamoto</surname><given-names>M.</given-names></name><name><surname>Dan</surname><given-names>H.</given-names></name><name><surname>Jurcak</surname><given-names>V.</given-names></name><name><surname>Dan</surname><given-names>I.</given-names></name></person-group> (<year>2005</year>). <article-title>Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI</article-title>. <source>Neuroimage</source><volume>27</volume>, <fpage>842</fpage>–<lpage>851</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroimage.2005.05.019</pub-id><pub-id pub-id-type="pmid">15979346</pub-id></mixed-citation></ref><ref id="B54"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Smith</surname><given-names>D. V.</given-names></name><name><surname>Gseir</surname><given-names>M.</given-names></name><name><surname>Speer</surname><given-names>M. E.</given-names></name><name><surname>Delgado</surname><given-names>M. R.</given-names></name></person-group> (<year>2016</year>). <article-title>Toward a cumulative science of functional integration: a meta-analysis of psychophysiological interactions</article-title>. <source>Hum. Brain Mapp.</source><volume>37</volume>, <fpage>2904</fpage>–<lpage>2917</lpage>. <pub-id pub-id-type="doi">10.1002/hbm.23216</pub-id><pub-id pub-id-type="pmid">27145472</pub-id></mixed-citation></ref><ref id="B55"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Steinbrink</surname><given-names>J.</given-names></name><name><surname>Villringer</surname><given-names>A.</given-names></name><name><surname>Kempf</surname><given-names>F.</given-names></name><name><surname>Haux</surname><given-names>D.</given-names></name><name><surname>Boden</surname><given-names>S.</given-names></name><name><surname>Obrig</surname><given-names>H.</given-names></name></person-group> (<year>2006</year>). <article-title>Illuminating the BOLD signal: combined fMRI–fNIRS studies</article-title>. <source>Magn. Reson. Imaging</source><volume>24</volume>, <fpage>495</fpage>–<lpage>505</lpage>. <pub-id pub-id-type="doi">10.1016/j.mri.2005.12.034</pub-id><pub-id pub-id-type="pmid">16677956</pub-id></mixed-citation></ref><ref id="B56"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Strangman</surname><given-names>G.</given-names></name><name><surname>Culver</surname><given-names>J. P.</given-names></name><name><surname>Thompson</surname><given-names>J. H.</given-names></name><name><surname>Boas</surname><given-names>D. A.</given-names></name></person-group> (<year>2002</year>). <article-title>A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation</article-title>. <source>Neuroimage</source><volume>17</volume>, <fpage>719</fpage>–<lpage>731</lpage>. <pub-id pub-id-type="doi">10.1006/nimg.2002.1227</pub-id><pub-id pub-id-type="pmid">12377147</pub-id></mixed-citation></ref><ref id="B57"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Tak</surname><given-names>S.</given-names></name><name><surname>Ye</surname><given-names>J. C.</given-names></name></person-group> (<year>2014</year>). <article-title>Statistical analysis of fNIRS data: a comprehensive review</article-title>. <source>Neuroimage</source><volume>85</volume>, <fpage>72</fpage>–<lpage>91</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroimage.2013.06.016</pub-id><pub-id pub-id-type="pmid">23774396</pub-id></mixed-citation></ref><ref id="B58"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Testa</surname><given-names>R.</given-names></name><name><surname>Bennett</surname><given-names>P.</given-names></name><name><surname>Ponsford</surname><given-names>J.</given-names></name></person-group> (<year>2012</year>). <article-title>Factor analysis of nineteen executive function tests in a healthy adult population</article-title>. <source>Arch. Clin. Neuropsychol.</source><volume>27</volume>, <fpage>213</fpage>–<lpage>224</lpage>. <pub-id pub-id-type="doi">10.1093/arclin/acr112</pub-id><pub-id pub-id-type="pmid">22314610</pub-id></mixed-citation></ref><ref id="B59"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Tian</surname><given-names>L.</given-names></name><name><surname>Ma</surname><given-names>L.</given-names></name><name><surname>Wang</surname><given-names>L.</given-names></name></person-group> (<year>2016</year>). <article-title>Alterations of functional connectivities from early to middle adulthood: clues from multivariate pattern analysis of resting-state fMRI data</article-title>. <source>Neuroimage</source><volume>129</volume>, <fpage>389</fpage>–<lpage>400</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroimage.2016.01.039</pub-id><pub-id pub-id-type="pmid">26825442</pub-id></mixed-citation></ref><ref id="B60"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Tian</surname><given-names>L.</given-names></name><name><surname>Wang</surname><given-names>J.</given-names></name><name><surname>Yan</surname><given-names>C.</given-names></name><name><surname>He</surname><given-names>Y.</given-names></name></person-group> (<year>2011</year>). <article-title>Hemisphere- and gender-related differences in small-world brain networks: a resting-state functional MRI study</article-title>. <source>Neuroimage</source><volume>54</volume>, <fpage>191</fpage>–<lpage>202</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroimage.2010.07.066</pub-id><pub-id pub-id-type="pmid">20688177</pub-id></mixed-citation></ref><ref id="B61"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>van den Heuvel</surname><given-names>M. P.</given-names></name><name><surname>Pol</surname><given-names>H. E. H.</given-names></name></person-group> (<year>2010</year>). <article-title>Exploring the brain network: a review on resting-state fMRI functional connectivity</article-title>. <source>Eur. Neuropsychopharmacol.</source><volume>20</volume>, <fpage>519</fpage>–<lpage>534</lpage>. <pub-id pub-id-type="doi">10.1016/j.euroneuro.2010.03.008</pub-id><pub-id pub-id-type="pmid">20471808</pub-id></mixed-citation></ref><ref id="B62"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>van den Heuvel</surname><given-names>M. P.</given-names></name><name><surname>Stam</surname><given-names>C. J.</given-names></name><name><surname>Kahn</surname><given-names>R. S.</given-names></name><name><surname>Pol</surname><given-names>H. E. H.</given-names></name></person-group> (<year>2009</year>). <article-title>Efficiency of functional brain networks and intellectual performance</article-title>. <source>J. Neurosci.</source><volume>29</volume>, <fpage>7619</fpage>–<lpage>7624</lpage>. <pub-id pub-id-type="doi">10.1523/JNEUROSCI.1443-09.2009</pub-id><pub-id pub-id-type="pmid">19515930</pub-id></mixed-citation></ref><ref id="B63"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Vidal</surname><given-names>J.</given-names></name><name><surname>Mills</surname><given-names>T.</given-names></name><name><surname>Pang</surname><given-names>E. W.</given-names></name><name><surname>Taylor</surname><given-names>M. J.</given-names></name></person-group> (<year>2012</year>). <article-title>Response inhibition in adults and teenagers: spatiotemporal differences in the prefrontal cortex</article-title>. <source>Brain Cogn.</source><volume>79</volume>, <fpage>49</fpage>–<lpage>59</lpage>. <pub-id pub-id-type="doi">10.1016/j.bandc.2011.12.011</pub-id><pub-id pub-id-type="pmid">22325785</pub-id></mixed-citation></ref><ref id="B64"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Villringer</surname><given-names>A.</given-names></name><name><surname>Dirnagl</surname><given-names>U.</given-names></name></person-group> (<year>1994</year>). <article-title>Coupling of brain activity and cerebral blood flow: basis of functional neuroimaging</article-title>. <source>Cerebrovasc. Brain Metab. Rev.</source><volume>7</volume>, <fpage>240</fpage>–<lpage>276</lpage>. <pub-id pub-id-type="pmid">8519605</pub-id></mixed-citation></ref><ref id="B65"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wang</surname><given-names>L.</given-names></name><name><surname>Song</surname><given-names>M.</given-names></name><name><surname>Jiang</surname><given-names>T.</given-names></name><name><surname>Zhang</surname><given-names>Y.</given-names></name><name><surname>Yu</surname><given-names>C.</given-names></name></person-group> (<year>2011</year>). <article-title>Regional homogeneity of the resting-state brain activity correlates with individual intelligence</article-title>. <source>Neurosci. Lett.</source><volume>488</volume>, <fpage>275</fpage>–<lpage>278</lpage>. <pub-id pub-id-type="doi">10.1016/j.neulet.2010.11.046</pub-id><pub-id pub-id-type="pmid">21108990</pub-id></mixed-citation></ref><ref id="B66"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wang</surname><given-names>Z.</given-names></name><name><surname>Liu</surname><given-names>J.</given-names></name><name><surname>Zhong</surname><given-names>N.</given-names></name><name><surname>Qin</surname><given-names>Y.</given-names></name><name><surname>Zhou</surname><given-names>H.</given-names></name><name><surname>Li</surname><given-names>K.</given-names></name></person-group> (<year>2012</year>). <article-title>Changes in the brain intrinsic organization in both on-task state and post-task resting state</article-title>. <source>Neuroimage</source><volume>62</volume>, <fpage>394</fpage>–<lpage>407</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroimage.2012.04.051</pub-id><pub-id pub-id-type="pmid">22569542</pub-id></mixed-citation></ref><ref id="B67"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Watts</surname><given-names>D. J.</given-names></name><name><surname>Strogatz</surname><given-names>S. H.</given-names></name></person-group> (<year>1998</year>). <article-title>Collective dynamics of ‘small-world’ networks</article-title>. <source>Nature</source><volume>393</volume>, <fpage>440</fpage>–<lpage>442</lpage>. <pub-id pub-id-type="pmid">9623998</pub-id></mixed-citation></ref><ref id="B68"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Widjaja</surname><given-names>E.</given-names></name><name><surname>Zamyadi</surname><given-names>M.</given-names></name><name><surname>Raybaud</surname><given-names>C.</given-names></name><name><surname>Snead</surname><given-names>O. C.</given-names></name><name><surname>Smith</surname><given-names>M. L.</given-names></name></person-group> (<year>2013</year>). <article-title>Abnormal functional network connectivity among resting-state networks in children with frontal lobe epilepsy</article-title>. <source>Am. J. Neuroradiol.</source><volume>34</volume>, <fpage>2386</fpage>–<lpage>2392</lpage>. <pub-id pub-id-type="doi">10.3174/ajnr.a3608</pub-id><pub-id pub-id-type="pmid">23868148</pub-id></mixed-citation></ref><ref id="B69"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Woo</surname><given-names>B. K.</given-names></name><name><surname>Harwood</surname><given-names>D. G.</given-names></name><name><surname>Melrose</surname><given-names>R. J.</given-names></name><name><surname>Mandelkern</surname><given-names>M. A.</given-names></name><name><surname>Campa</surname><given-names>O. M.</given-names></name><name><surname>Walston</surname><given-names>A.</given-names></name><etal></etal></person-group>. (<year>2010</year>). <article-title>Executive deficits and regional brain metabolism in Alzheimer's disease</article-title>. <source>Int. J. Geriatr. Psychiatr</source>, <volume>25</volume>, <fpage>1150</fpage>–<lpage>1158</lpage>. <pub-id pub-id-type="doi">10.1002/gps.2452</pub-id><pub-id pub-id-type="pmid">20069587</pub-id></mixed-citation></ref><ref id="B70"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wu</surname><given-names>L.</given-names></name><name><surname>Soder</surname><given-names>R. B.</given-names></name><name><surname>Schoemaker</surname><given-names>D.</given-names></name><name><surname>Carbonnell</surname><given-names>F.</given-names></name><name><surname>Sziklas</surname><given-names>V.</given-names></name><name><surname>Rowley</surname><given-names>J.</given-names></name><etal></etal></person-group>. (<year>2014</year>). <article-title>Resting state executive control network adaptations in amnestic mild cognitive impairment</article-title>. <source>J. Alzheimers Dis.</source><volume>40</volume>, <fpage>993</fpage>–<lpage>1004</lpage>. <pub-id pub-id-type="doi">10.3233/JAD-131574</pub-id><pub-id pub-id-type="pmid">24583406</pub-id></mixed-citation></ref><ref id="B71"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Xu</surname><given-names>P.</given-names></name><name><surname>Huang</surname><given-names>R.</given-names></name><name><surname>Wang</surname><given-names>J.</given-names></name><name><surname>Van Dam</surname><given-names>N. T.</given-names></name><name><surname>Xie</surname><given-names>T.</given-names></name><name><surname>Dong</surname><given-names>Z.</given-names></name><etal></etal></person-group>. (<year>2014</year>). <article-title>Different topological organization of human brain functional networks with eyes open versus eyes closed</article-title>. <source>Neuroimage</source><volume>90</volume>, <fpage>246</fpage>–<lpage>255</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroimage.2013.12.060</pub-id><pub-id pub-id-type="pmid">24434242</pub-id></mixed-citation></ref><ref id="B72"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ye</surname><given-names>J.</given-names></name><name><surname>Tak</surname><given-names>S.</given-names></name><name><surname>Jang</surname><given-names>K.</given-names></name><name><surname>Jung</surname><given-names>J.</given-names></name><name><surname>Jang</surname><given-names>J.</given-names></name></person-group> (<year>2009</year>). <article-title>NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy</article-title>. <source>Neuroimage</source><volume>44</volume>, <fpage>428</fpage>–<lpage>447</lpage>. <pub-id pub-id-type="doi">10.1016/j.neuroimage.2008.08.036</pub-id><pub-id pub-id-type="pmid">18848897</pub-id></mixed-citation></ref><ref id="B73"><mixed-citation publication-type="book"><person-group person-group-type="author"><name><surname>Zelazo</surname><given-names>P. D.</given-names></name><name><surname>Müller</surname><given-names>U.</given-names></name></person-group> (<year>2002</year>). <article-title>Executive function in typical and atypical development</article-title>, in <source>Blackwell Handbook of Child Cognitive Development</source>, ed <person-group person-group-type="editor"><name><surname>Goswami</surname><given-names>U.</given-names></name></person-group> (<publisher-loc>Malden, MA</publisher-loc>: <publisher-name>Blackwell Publishing</publisher-name>), <fpage>445</fpage>–<lpage>469</lpage>.</mixed-citation></ref><ref id="B74"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zou</surname><given-names>Q.</given-names></name><name><surname>Ross</surname><given-names>T. J.</given-names></name><name><surname>Gu</surname><given-names>H.</given-names></name><name><surname>Geng</surname><given-names>X.</given-names></name><name><surname>Zuo</surname><given-names>X. N.</given-names></name><name><surname>Hong</surname><given-names>L. E.</given-names></name><etal></etal></person-group>. (<year>2013</year>). <article-title>Intrinsic resting-state activity predicts working memory brain activation and behavioral performance</article-title>. <source>Hum. Brain Mapp.</source><volume>34</volume>, <fpage>3204</fpage>–<lpage>3215</lpage>. <pub-id pub-id-type="doi">10.1002/hbm.22136</pub-id><pub-id pub-id-type="pmid">22711376</pub-id></mixed-citation></ref></ref-list></back></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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